{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "82f83885",
   "metadata": {},
   "source": [
    "# Python Analysis for INFEWS Solar Analysis\n",
    "#### By Ted Koch and Peter Woodbury\n",
    "#### Code Written by Ted Koch\n",
    "The code in this document completes the bulk of the data processing and graphing for the analysis completed for our paper, \"title\". Additional analysis was completed in arcgis, the specific steps for which are not included here, but the methods are in our paper. \n",
    "The processes completed in this code are annotated to be as clear as possible, however this is a long and complex analysis, so not everything may be perfectly explained, feel free to contact Ted Koch at twk54@cornell.edu for questions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c85d83a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "#########################################################\n",
    "# Importing libraries, defining paths and setting styling\n",
    "#########################################################\n",
    "import pandas as pd\n",
    "import geopandas as gpd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from scipy import stats\n",
    "from scipy.stats import ttest_ind, linregress\n",
    "from shapely.ops import nearest_points\n",
    "import fiona\n",
    "import os\n",
    "import sklearn\n",
    "from sklearn.metrics import r2_score\n",
    "from docx import Document\n",
    "import re\n",
    "from matplotlib.ticker import FormatStrFormatter, StrMethodFormatter\n",
    "import matplotlib.ticker as ticker\n",
    "from openpyxl import load_workbook\n",
    "from openpyxl.styles import Font\n",
    "from shapely.geometry import Polygon\n",
    "from scipy.optimize import curve_fit, minimize\n",
    "import statsmodels.api as sm\n",
    "from collections import Counter\n",
    "import statistics\n",
    "import seaborn as sns\n",
    "import matplotlib\n",
    "import matplotlib.patches as mpatches\n",
    "\n",
    "import os\n",
    "import time\n",
    "from selenium import webdriver\n",
    "from selenium.webdriver.common.by import By\n",
    "from selenium.webdriver.chrome.service import Service\n",
    "from selenium.webdriver.chrome.options import Options\n",
    "from bs4 import BeautifulSoup\n",
    "import pandas as pd\n",
    "import re\n",
    "import geopandas as gpd\n",
    "import pandas as pd\n",
    "from shapely import wkt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "50cb5578",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Set location path of the script\n",
    "root_dir = \"path/to/the/location/of/the/publish/folder\"\n",
    "gdb_path = f'{root_dir}/Data/INFEWS_Solar.gpkg'\n",
    "figures = f'{root_dir}/Outputs/Figures'\n",
    "tables = f\"{root_dir}/Data/Tables\"\n",
    "#One Column Graph Dimensions\n",
    "One_x_dim, One_y_dim = 3.5, 3.5\n",
    "#Two Column Graph Dimensions\n",
    "Two_x_dim, Two_y_dim = 5.5, 5\n",
    "#Three Column Graph Dimensions\n",
    "Three_x_dim , Three_y_dim = 7.5, 5.5\n",
    "\n",
    "#Font Properties\n",
    "font_properties = {\n",
    "    'family': 'Times New Roman',  # Choose font family (e.g., 'sans-serif', 'serif', 'monospace')\n",
    "    'style': 'normal',  # Choose font style (e.g., 'normal', 'italic', 'oblique')\n",
    "    'weight': 'normal',   # Choose font weight (e.g., 'normal', 'bold', 'light')\n",
    "    'size': 12          # Font size\n",
    "}\n",
    "\n",
    "#Store global variables such as image outpUT folder\n",
    "output_folder = fr'{root_dir}\\Figures\\Graphs'\n",
    "gdb_path = fr\"{root_dir}\\Data\\INFEWS_Solar.gpkg\"\n",
    "#####################\n",
    "# Define Color Values\n",
    "#####################\n",
    "# Gray Blue\n",
    "Light_Gray_Blue = '#97a7c4'\n",
    "Dark_Gray_Blue = '#374860'\n",
    "#Gray\n",
    "Gray1 = '#f7f7f7'\n",
    "Gray2= '#cccccc'\n",
    "Gray3 = '#969696'\n",
    "Gray4 = '#636363'\n",
    "Gray5 = '#252525'\n",
    "#Red Orange\n",
    "Red1 = '#ffffb2'\n",
    "Red2 = '#fecc5c'\n",
    "Red3 = '#fd8d3c'\n",
    "Red4 = '#f03b20'\n",
    "Red5 = '#bd0026'\n",
    "#Blue Green\n",
    "GB1 = '#emerged8fb'\n",
    "GB2 = '#b2e2e2'\n",
    "GB3 = '#66c2a4'\n",
    "GB4 = '#2ca25f'\n",
    "GB5 = '#006d2c'\n",
    "#2 Color\n",
    "C2_1 = ['#0000ff', '#ff0000', '#009a04']\n",
    "#Overlapping\n",
    "Ovlp2= ['#ff0000', '#ffc000']\n",
    "Ovlp3 = ['#cc00cc', '#ff0000', '#ffc000']\n",
    "Ovlp4 = ['#0070c0', '#ffff00', '#ff0000' ]\n",
    "output_folder = f'{root_dir}\\Figures\\Graphs'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3dca9158",
   "metadata": {},
   "source": [
    "### Load in Open NY solar data and determine site type from modeled capacity factor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "f3f6b6d7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Site Type</th>\n",
       "      <th>Street Address</th>\n",
       "      <th>City</th>\n",
       "      <th>ZIP Code</th>\n",
       "      <th>Capacity kW DC</th>\n",
       "      <th>Georeference</th>\n",
       "      <th>Date Application Received</th>\n",
       "      <th>Date Completed</th>\n",
       "      <th>Project Status</th>\n",
       "      <th>Address</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>FA</td>\n",
       "      <td>201 Cary Rd</td>\n",
       "      <td>Mechanicville</td>\n",
       "      <td>12118</td>\n",
       "      <td>1000.35</td>\n",
       "      <td>POINT (-73.74008015 42.918470562)</td>\n",
       "      <td>08/22/2016</td>\n",
       "      <td>06/28/2016</td>\n",
       "      <td>Complete</td>\n",
       "      <td>201 Cary Rd Mechanicville 12118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>FA</td>\n",
       "      <td>1 Rockwood Rd</td>\n",
       "      <td>Tarrytown</td>\n",
       "      <td>10591</td>\n",
       "      <td>1024.65</td>\n",
       "      <td>POINT (-73.86190713 41.115117022)</td>\n",
       "      <td>02/13/2018</td>\n",
       "      <td>07/26/2018</td>\n",
       "      <td>Complete</td>\n",
       "      <td>1 Rockwood Rd Tarrytown 10591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>FA</td>\n",
       "      <td>9801 Avenue D</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>11236</td>\n",
       "      <td>1055.70</td>\n",
       "      <td>POINT (-73.906834376 40.650475673)</td>\n",
       "      <td>02/13/2018</td>\n",
       "      <td>12/27/2018</td>\n",
       "      <td>Complete</td>\n",
       "      <td>9801 Avenue D Brooklyn 11236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>FA</td>\n",
       "      <td>2900 Purchase St</td>\n",
       "      <td>Purchase</td>\n",
       "      <td>10577</td>\n",
       "      <td>1085.40</td>\n",
       "      <td>POINT (-73.712954537 41.030432205)</td>\n",
       "      <td>03/03/2017</td>\n",
       "      <td>02/20/2019</td>\n",
       "      <td>Complete</td>\n",
       "      <td>2900 Purchase St Purchase 10577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>FA</td>\n",
       "      <td>6226 Lake Rd</td>\n",
       "      <td>Sodus</td>\n",
       "      <td>14551</td>\n",
       "      <td>1088.00</td>\n",
       "      <td>POINT (-77.086176383 43.271514805)</td>\n",
       "      <td>02/01/2017</td>\n",
       "      <td>03/07/2018</td>\n",
       "      <td>Complete</td>\n",
       "      <td>6226 Lake Rd Sodus 14551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1251</th>\n",
       "      <td>SAT</td>\n",
       "      <td>00 Wheeler Road</td>\n",
       "      <td>Elbridge</td>\n",
       "      <td>13060</td>\n",
       "      <td>8246.94</td>\n",
       "      <td>NaN</td>\n",
       "      <td>05/01/2024</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Pipeline</td>\n",
       "      <td>00 Wheeler Road Elbridge 13060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1252</th>\n",
       "      <td>SAT</td>\n",
       "      <td>2191 Brookview Rd</td>\n",
       "      <td>Castleton on Hudson</td>\n",
       "      <td>12033</td>\n",
       "      <td>8310.96</td>\n",
       "      <td>POINT (-73.708734665 42.554403284)</td>\n",
       "      <td>09/24/2019</td>\n",
       "      <td>09/14/2023</td>\n",
       "      <td>Complete</td>\n",
       "      <td>2191 Brookview Rd Castleton on Hudson 12033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1253</th>\n",
       "      <td>SAT</td>\n",
       "      <td>13590 Duanesburg Rd</td>\n",
       "      <td>Delanson</td>\n",
       "      <td>12053</td>\n",
       "      <td>8310.96</td>\n",
       "      <td>POINT (-74.250625977 42.724373236)</td>\n",
       "      <td>10/08/2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Pipeline</td>\n",
       "      <td>13590 Duanesburg Rd Delanson 12053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1254</th>\n",
       "      <td>SAT</td>\n",
       "      <td>Upper Ravine Rd</td>\n",
       "      <td>Norwich</td>\n",
       "      <td>13815</td>\n",
       "      <td>8530.34</td>\n",
       "      <td>POINT (-75.542209352 42.511111801)</td>\n",
       "      <td>07/03/2024</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Pipeline</td>\n",
       "      <td>Upper Ravine Rd Norwich 13815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1255</th>\n",
       "      <td>FA</td>\n",
       "      <td>1096 State Route 149</td>\n",
       "      <td>Queensbury</td>\n",
       "      <td>12804</td>\n",
       "      <td>9111.96</td>\n",
       "      <td>POINT (-73.614148599 43.394706887)</td>\n",
       "      <td>05/31/2023</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Pipeline</td>\n",
       "      <td>1096 State Route 149 Queensbury 12804</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1134 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Site Type        Street Address                 City  ZIP Code  \\\n",
       "0           FA           201 Cary Rd        Mechanicville     12118   \n",
       "8           FA         1 Rockwood Rd            Tarrytown     10591   \n",
       "9           FA         9801 Avenue D             Brooklyn     11236   \n",
       "12          FA      2900 Purchase St             Purchase     10577   \n",
       "13          FA          6226 Lake Rd                Sodus     14551   \n",
       "...        ...                   ...                  ...       ...   \n",
       "1251       SAT       00 Wheeler Road             Elbridge     13060   \n",
       "1252       SAT     2191 Brookview Rd  Castleton on Hudson     12033   \n",
       "1253       SAT   13590 Duanesburg Rd             Delanson     12053   \n",
       "1254       SAT       Upper Ravine Rd              Norwich     13815   \n",
       "1255        FA  1096 State Route 149           Queensbury     12804   \n",
       "\n",
       "      Capacity kW DC                        Georeference  \\\n",
       "0            1000.35   POINT (-73.74008015 42.918470562)   \n",
       "8            1024.65   POINT (-73.86190713 41.115117022)   \n",
       "9            1055.70  POINT (-73.906834376 40.650475673)   \n",
       "12           1085.40  POINT (-73.712954537 41.030432205)   \n",
       "13           1088.00  POINT (-77.086176383 43.271514805)   \n",
       "...              ...                                 ...   \n",
       "1251         8246.94                                 NaN   \n",
       "1252         8310.96  POINT (-73.708734665 42.554403284)   \n",
       "1253         8310.96  POINT (-74.250625977 42.724373236)   \n",
       "1254         8530.34  POINT (-75.542209352 42.511111801)   \n",
       "1255         9111.96  POINT (-73.614148599 43.394706887)   \n",
       "\n",
       "     Date Application Received Date Completed Project Status  \\\n",
       "0                   08/22/2016     06/28/2016       Complete   \n",
       "8                   02/13/2018     07/26/2018       Complete   \n",
       "9                   02/13/2018     12/27/2018       Complete   \n",
       "12                  03/03/2017     02/20/2019       Complete   \n",
       "13                  02/01/2017     03/07/2018       Complete   \n",
       "...                        ...            ...            ...   \n",
       "1251                05/01/2024            NaN       Pipeline   \n",
       "1252                09/24/2019     09/14/2023       Complete   \n",
       "1253                10/08/2019            NaN       Pipeline   \n",
       "1254                07/03/2024            NaN       Pipeline   \n",
       "1255                05/31/2023            NaN       Pipeline   \n",
       "\n",
       "                                          Address  \n",
       "0                 201 Cary Rd Mechanicville 12118  \n",
       "8                   1 Rockwood Rd Tarrytown 10591  \n",
       "9                    9801 Avenue D Brooklyn 11236  \n",
       "12                2900 Purchase St Purchase 10577  \n",
       "13                       6226 Lake Rd Sodus 14551  \n",
       "...                                           ...  \n",
       "1251               00 Wheeler Road Elbridge 13060  \n",
       "1252  2191 Brookview Rd Castleton on Hudson 12033  \n",
       "1253           13590 Duanesburg Rd Delanson 12053  \n",
       "1254                Upper Ravine Rd Norwich 13815  \n",
       "1255        1096 State Route 149 Queensbury 12804  \n",
       "\n",
       "[1134 rows x 10 columns]"
      ]
     },
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     "output_type": "display_data"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "csv= fr\"{tables}\\Solar_Electric_Programs_Reported_by_NYSERDA__Beginning_2000.csv\"\n",
    "ders = pd.read_csv(csv)\n",
    "ders[\"Modeled Capacity Factor\"]= ders['Expected KWh Annual Production']/(ders['Total Nameplate kW DC']*8766)\n",
    "# Set 'SAT' where Modeled Capacity Factor is close to 0.159890\n",
    "ders.loc[np.isclose(ders['Modeled Capacity Factor'], 0.159890), 'Site Type'] = 'SAT'\n",
    "ders.hist(column='Modeled Capacity Factor')\n",
    "\n",
    "# Set 'FA' where Modeled Capacity Factor is close to 0.1339\n",
    "ders.loc[np.isclose(ders['Modeled Capacity Factor'], 0.133908), 'Site Type'] = 'FA'\n",
    "\n",
    "ders = ders.dropna(subset=['Site Type'])\n",
    "ders['Capacity kW DC'] = ders['Total Nameplate kW DC']\n",
    "ders= ders[['Site Type', 'Street Address', 'City', 'ZIP Code', 'Capacity kW DC', 'Georeference', 'Date Application Received', 'Date Completed', 'Project Status']]\n",
    "ders['Address']= ders['Street Address'] + ' ' + ders['City'] + ' ' + ders['ZIP Code'].astype(str)\n",
    "display(ders)\n",
    "ders.to_csv(f'{tables}/Outputs/DER_ONY.csv')\n",
    "\n",
    "# Drop rows where Georeference is NaN or not a string\n",
    "ders = ders[ders['Georeference'].apply(lambda x: isinstance(x, str))].copy()\n",
    "\n",
    "# Convert WKT strings to geometry\n",
    "ders['geometry'] = ders['Georeference'].apply(wkt.loads)\n",
    "\n",
    "# Convert to GeoDataFrame\n",
    "gdf = gpd.GeoDataFrame(ders, geometry='geometry', crs='EPSG:4326')\n",
    "\n",
    "# Save to GeoPackage\n",
    "gdf.to_file(gdb_path, layer=\"DER_Points\", driver=\"GPKG\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eee26c75",
   "metadata": {},
   "source": [
    "### USS Development Trends\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "id": "59a0309b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 550x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total installed capacity in dataset: 5523.51 MW\n"
     ]
    }
   ],
   "source": [
    "# Optional: font and color definitions from your previous code\n",
    "font_properties = {\n",
    "    'family': 'Times New Roman',\n",
    "    'style': 'normal',\n",
    "    'weight': 'normal',\n",
    "    'size': 18\n",
    "}\n",
    "\n",
    "x_dim, y_dim = Two_x_dim, Two_y_dim\n",
    "\n",
    "graph_name = 'Cumulative_Installed_Capacity'\n",
    "\n",
    "# Parse date columns\n",
    "ders['Date Application Received'] = pd.to_datetime(ders['Date Application Received'], errors='coerce')\n",
    "ders['Date Completed'] = pd.to_datetime(ders['Date Completed'], errors='coerce')\n",
    "\n",
    "# Function to calculate cumulative capacity by year and type\n",
    "def get_cumulative(df, date_col, site_type):\n",
    "    subset = df[df['Site Type'] == site_type].dropna(subset=[date_col])\n",
    "    subset = subset.copy()\n",
    "    subset['Year'] = subset[date_col].dt.year\n",
    "    grouped = subset.groupby('Year')['Capacity kW DC'].sum().sort_index().cumsum().reset_index()\n",
    "    grouped.columns = ['Year', 'Cumulative Capacity']\n",
    "    grouped['Cumulative Capacity'] /= 1000  # Convert to MW\n",
    "    return grouped\n",
    "\n",
    "# Get data for plots\n",
    "sat_counts_app = get_cumulative(ders, 'Date Application Received', 'SAT')\n",
    "fa_counts_app = get_cumulative(ders, 'Date Application Received', 'FA')\n",
    "sat_counts_comp = get_cumulative(ders, 'Date Completed', 'SAT')\n",
    "fa_counts_comp = get_cumulative(ders, 'Date Completed', 'FA')\n",
    "\n",
    "# Plotting\n",
    "plt.figure(figsize=(x_dim, y_dim))\n",
    "\n",
    "plt.plot(fa_counts_comp['Year'], fa_counts_comp['Cumulative Capacity'], label='FA', marker='o', color=C2_1[1])\n",
    "plt.plot(sat_counts_comp['Year'], sat_counts_comp['Cumulative Capacity'], label='SAT', marker='^', color=C2_1[0])\n",
    "plt.plot(fa_counts_app['Year'], fa_counts_app['Cumulative Capacity'], label='FA Incomplete', marker='o', linestyle=':', color=C2_1[1])\n",
    "plt.plot(sat_counts_app['Year'], sat_counts_app['Cumulative Capacity'], label='SAT Incomplete', marker='^', linestyle=':', color=C2_1[0])\n",
    "\n",
    "plt.xlabel('Year', fontdict=font_properties)\n",
    "plt.ylabel('Statewide Capacity (MW$_{\\mathrm{DC}}$)', fontdict=font_properties)\n",
    "\n",
    "plt.legend(prop=font_properties)\n",
    "plt.grid(True)\n",
    "plt.tight_layout()\n",
    "\n",
    "# Format y-axis\n",
    "ax = plt.gca()\n",
    "ax.yaxis.set_major_formatter(ticker.FuncFormatter(lambda x, pos: f'{x:,.0f}'))\n",
    "\n",
    "# Save and show\n",
    "plt.savefig(f'{figures}/{graph_name}.jpg', dpi=600, format='jpg')\n",
    "plt.show()\n",
    "\n",
    "# Max capacity (optional)\n",
    "installed_capacity = ders['Capacity kW DC'].sum() / 1000  # Total in MW\n",
    "print(f'Total installed capacity in dataset: {installed_capacity:.2f} MW')\n",
    "\n",
    "# --- New section: Save total completed cumulative capacity ---\n",
    "# Merge SAT and FA cumulative completed capacities by year\n",
    "combined = pd.merge(\n",
    "    sat_counts_comp, fa_counts_comp,\n",
    "    on='Year', how='outer', suffixes=('_SAT', '_FA')\n",
    ").sort_values('Year')\n",
    "\n",
    "# Fill any missing values with 0\n",
    "combined[['Cumulative Capacity_SAT', 'Cumulative Capacity_FA']] = combined[[\n",
    "    'Cumulative Capacity_SAT', 'Cumulative Capacity_FA'\n",
    "]].fillna(0)\n",
    "\n",
    "# Sum the SAT and FA capacities to get the total\n",
    "combined['Total Cumulative Capacity'] = (\n",
    "    combined['Cumulative Capacity_SAT'] + combined['Cumulative Capacity_FA']\n",
    ")\n",
    "\n",
    "# Save the result to CSV\n",
    "combined[['Year', 'Total Cumulative Capacity']].to_csv( f\"{tables}/Outputs/Completed_Capacity_Over_Time.csv\", index=False\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7616a87",
   "metadata": {},
   "source": [
    "### Process scrapped DER website data and trim capacity factors to the nearest year\n",
    "The DER data in NYSERDA DER Integrated Data System(full).csv was scraped from site pages at https://der.nyserda.ny.gov/search/ using Octoparse."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "0adb55a9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Capacity</th>\n",
       "      <th>SiteName</th>\n",
       "      <th>Address</th>\n",
       "      <th>Generated</th>\n",
       "      <th>Units</th>\n",
       "      <th>Date Range</th>\n",
       "      <th>Month/Year</th>\n",
       "      <th>Generated (Monthly)</th>\n",
       "      <th>CF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4,170 kW</td>\n",
       "      <td>103 Sparling Road, LLC</td>\n",
       "      <td>103 Sparling Rd Saugerties, NY 12477</td>\n",
       "      <td>15,307</td>\n",
       "      <td>MWh</td>\n",
       "      <td>Jun 2022 to May 2025</td>\n",
       "      <td>Jun-2022</td>\n",
       "      <td>4094</td>\n",
       "      <td>2.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4,170 kW</td>\n",
       "      <td>103 Sparling Road, LLC</td>\n",
       "      <td>103 Sparling Rd Saugerties, NY 12477</td>\n",
       "      <td>15,307</td>\n",
       "      <td>MWh</td>\n",
       "      <td>Jun 2022 to May 2025</td>\n",
       "      <td>Jul-2022</td>\n",
       "      <td>347256</td>\n",
       "      <td>11.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4,170 kW</td>\n",
       "      <td>103 Sparling Road, LLC</td>\n",
       "      <td>103 Sparling Rd Saugerties, NY 12477</td>\n",
       "      <td>15,307</td>\n",
       "      <td>MWh</td>\n",
       "      <td>Jun 2022 to May 2025</td>\n",
       "      <td>Aug-2022</td>\n",
       "      <td>712019</td>\n",
       "      <td>22.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4,170 kW</td>\n",
       "      <td>103 Sparling Road, LLC</td>\n",
       "      <td>103 Sparling Rd Saugerties, NY 12477</td>\n",
       "      <td>15,307</td>\n",
       "      <td>MWh</td>\n",
       "      <td>Jun 2022 to May 2025</td>\n",
       "      <td>Sep-2022</td>\n",
       "      <td>524995</td>\n",
       "      <td>17.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4,170 kW</td>\n",
       "      <td>103 Sparling Road, LLC</td>\n",
       "      <td>103 Sparling Rd Saugerties, NY 12477</td>\n",
       "      <td>15,307</td>\n",
       "      <td>MWh</td>\n",
       "      <td>Jun 2022 to May 2025</td>\n",
       "      <td>Oct-2022</td>\n",
       "      <td>390385</td>\n",
       "      <td>12.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29367</th>\n",
       "      <td>3,457 kW</td>\n",
       "      <td>Wildcat Renewables, LLC - 7506 Baird Rd</td>\n",
       "      <td>7506 Baird Road Minoa, NY 13116</td>\n",
       "      <td>8,760</td>\n",
       "      <td>MWh</td>\n",
       "      <td>Jul 2024 to May 2025</td>\n",
       "      <td>Jan-2025</td>\n",
       "      <td>128729</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29368</th>\n",
       "      <td>3,457 kW</td>\n",
       "      <td>Wildcat Renewables, LLC - 7506 Baird Rd</td>\n",
       "      <td>7506 Baird Road Minoa, NY 13116</td>\n",
       "      <td>8,760</td>\n",
       "      <td>MWh</td>\n",
       "      <td>Jul 2024 to May 2025</td>\n",
       "      <td>Feb-2025</td>\n",
       "      <td>147234</td>\n",
       "      <td>6.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29369</th>\n",
       "      <td>3,457 kW</td>\n",
       "      <td>Wildcat Renewables, LLC - 7506 Baird Rd</td>\n",
       "      <td>7506 Baird Road Minoa, NY 13116</td>\n",
       "      <td>8,760</td>\n",
       "      <td>MWh</td>\n",
       "      <td>Jul 2024 to May 2025</td>\n",
       "      <td>Mar-2025</td>\n",
       "      <td>353384</td>\n",
       "      <td>13.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29370</th>\n",
       "      <td>3,457 kW</td>\n",
       "      <td>Wildcat Renewables, LLC - 7506 Baird Rd</td>\n",
       "      <td>7506 Baird Road Minoa, NY 13116</td>\n",
       "      <td>8,760</td>\n",
       "      <td>MWh</td>\n",
       "      <td>Jul 2024 to May 2025</td>\n",
       "      <td>Apr-2025</td>\n",
       "      <td>392421</td>\n",
       "      <td>15.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29371</th>\n",
       "      <td>3,457 kW</td>\n",
       "      <td>Wildcat Renewables, LLC - 7506 Baird Rd</td>\n",
       "      <td>7506 Baird Road Minoa, NY 13116</td>\n",
       "      <td>8,760</td>\n",
       "      <td>MWh</td>\n",
       "      <td>Jul 2024 to May 2025</td>\n",
       "      <td>May-2025</td>\n",
       "      <td>52709</td>\n",
       "      <td>7.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>29372 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Capacity                                 SiteName  \\\n",
       "0      4,170 kW                   103 Sparling Road, LLC   \n",
       "1      4,170 kW                   103 Sparling Road, LLC   \n",
       "2      4,170 kW                   103 Sparling Road, LLC   \n",
       "3      4,170 kW                   103 Sparling Road, LLC   \n",
       "4      4,170 kW                   103 Sparling Road, LLC   \n",
       "...         ...                                      ...   \n",
       "29367  3,457 kW  Wildcat Renewables, LLC - 7506 Baird Rd   \n",
       "29368  3,457 kW  Wildcat Renewables, LLC - 7506 Baird Rd   \n",
       "29369  3,457 kW  Wildcat Renewables, LLC - 7506 Baird Rd   \n",
       "29370  3,457 kW  Wildcat Renewables, LLC - 7506 Baird Rd   \n",
       "29371  3,457 kW  Wildcat Renewables, LLC - 7506 Baird Rd   \n",
       "\n",
       "                                    Address Generated Units  \\\n",
       "0      103 Sparling Rd Saugerties, NY 12477    15,307   MWh   \n",
       "1      103 Sparling Rd Saugerties, NY 12477    15,307   MWh   \n",
       "2      103 Sparling Rd Saugerties, NY 12477    15,307   MWh   \n",
       "3      103 Sparling Rd Saugerties, NY 12477    15,307   MWh   \n",
       "4      103 Sparling Rd Saugerties, NY 12477    15,307   MWh   \n",
       "...                                     ...       ...   ...   \n",
       "29367       7506 Baird Road Minoa, NY 13116     8,760   MWh   \n",
       "29368       7506 Baird Road Minoa, NY 13116     8,760   MWh   \n",
       "29369       7506 Baird Road Minoa, NY 13116     8,760   MWh   \n",
       "29370       7506 Baird Road Minoa, NY 13116     8,760   MWh   \n",
       "29371       7506 Baird Road Minoa, NY 13116     8,760   MWh   \n",
       "\n",
       "                 Date Range Month/Year Generated (Monthly)    CF  \n",
       "0      Jun 2022 to May 2025   Jun-2022                4094   2.1  \n",
       "1      Jun 2022 to May 2025   Jul-2022              347256  11.2  \n",
       "2      Jun 2022 to May 2025   Aug-2022              712019  22.9  \n",
       "3      Jun 2022 to May 2025   Sep-2022              524995  17.5  \n",
       "4      Jun 2022 to May 2025   Oct-2022              390385  12.6  \n",
       "...                     ...        ...                 ...   ...  \n",
       "29367  Jul 2024 to May 2025   Jan-2025              128729   5.0  \n",
       "29368  Jul 2024 to May 2025   Feb-2025              147234   6.3  \n",
       "29369  Jul 2024 to May 2025   Mar-2025              353384  13.7  \n",
       "29370  Jul 2024 to May 2025   Apr-2025              392421  15.8  \n",
       "29371  Jul 2024 to May 2025   May-2025               52709   7.9  \n",
       "\n",
       "[29372 rows x 9 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\twk54\\AppData\\Local\\Temp\\ipykernel_40336\\2885176617.py:53: FutureWarning: errors='ignore' is deprecated and will raise in a future version. Use to_datetime without passing `errors` and catch exceptions explicitly instead\n",
      "  final_merged['Date'] = pd.to_datetime(final_merged['Month/Year'], format='%b-%Y', errors='ignore')\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import re\n",
    "\n",
    "# Load your CSV\n",
    "merged = pd.read_csv(f\"{tables}/NYSERDA DER Integrated Data System(full).csv\")\n",
    "\n",
    "def clean_name(name):\n",
    "    return str(name).splitlines()[0]\n",
    "\n",
    "def clean_address(address):\n",
    "    return ' '.join([line.strip() for line in str(address).splitlines() if line.strip()])\n",
    "\n",
    "def parse_mini_table(mini_table_text):\n",
    "    # Remove unwanted characters and split into chunks of 3\n",
    "    lines = [line.strip() for line in str(mini_table_text).splitlines() if line.strip()]\n",
    "    entries = []\n",
    "    for i in range(0, len(lines), 3):\n",
    "        try:\n",
    "            date = lines[i]\n",
    "            generated = lines[i+1].replace(',', '')  # remove commas from numbers\n",
    "            cf = lines[i+2]\n",
    "            entries.append((date, generated, cf))\n",
    "        except IndexError:\n",
    "            continue  # skip incomplete rows\n",
    "    return entries\n",
    "\n",
    "# List to store expanded rows\n",
    "expanded_rows = []\n",
    "\n",
    "# Process each row\n",
    "for _, row in merged.iterrows():\n",
    "    base_data = {\n",
    "        'Capacity': row['Capacity'],\n",
    "        'SiteName': clean_name(row['SiteName']),\n",
    "        'Address': clean_address(row['Address']),\n",
    "        'Generated': row['Generated'],\n",
    "        'Units': row['Generated_Units'],\n",
    "        'Date Range': row['Date_Range']\n",
    "    }\n",
    "    mini_table = parse_mini_table(row['Data'])\n",
    "    for date, generated, cf in mini_table:\n",
    "        new_row = base_data.copy()\n",
    "        new_row['Month/Year'] = date.replace(\" \", \"-\")\n",
    "        new_row['Generated (Monthly)'] = generated\n",
    "        new_row['CF'] = cf\n",
    "        expanded_rows.append(new_row)\n",
    "\n",
    "# Create final DataFrame\n",
    "final_merged = pd.DataFrame(expanded_rows)\n",
    "display(final_merged)\n",
    "\n",
    "# Convert Month/Year to datetime\n",
    "final_merged['Date'] = pd.to_datetime(final_merged['Month/Year'], format='%b-%Y', errors='ignore')\n",
    "\n",
    "# Container for trimmed data\n",
    "trimmed_groups = []\n",
    "\n",
    "# Group by site\n",
    "for site, group in final_merged.groupby('SiteName'):\n",
    "    group = group.sort_values('Date').reset_index(drop=True)\n",
    "    n = len(group)\n",
    "    full_years = n // 12\n",
    "    rows_to_keep = full_years * 12\n",
    "\n",
    "    if rows_to_keep == 0:\n",
    "        continue  # skip if no full year of data\n",
    "\n",
    "    trimmed_group = group.iloc[:rows_to_keep].copy()\n",
    "    trimmed_groups.append(trimmed_group)\n",
    "\n",
    "# Combine all trimmed data\n",
    "trimmed_df = pd.concat(trimmed_groups).reset_index(drop=True)\n",
    "# Ensure CF is numeric\n",
    "# Ensure CF is numeric\n",
    "trimmed_df['CF'] = pd.to_numeric(trimmed_df['CF'], errors='coerce')\n",
    "\n",
    "# Compute average CF per site\n",
    "site_avg_cf = (\n",
    "    trimmed_df\n",
    "    .groupby('SiteName')\n",
    "    .agg(\n",
    "        Avg_CF=('CF', 'mean'),\n",
    "        Months=('CF', 'count'),\n",
    "        Start_Date=('Date', 'min'),\n",
    "        End_Date=('Date', 'max')\n",
    "    )\n",
    "    .reset_index()\n",
    ")\n",
    "\n",
    "# Grab first occurrence of each site's metadata (assumes it's constant per site)\n",
    "site_metadata = (\n",
    "    trimmed_df\n",
    "    .drop_duplicates(subset='SiteName')\n",
    "    [['SiteName', 'Capacity', 'Address']]\n",
    ")\n",
    "\n",
    "# Merge metadata with average CF\n",
    "der_cf = pd.merge(site_metadata, site_avg_cf, on='SiteName')\n",
    "\n",
    "# Reorder columns for clarity\n",
    "der_cf= der_cf[['SiteName', 'Address', 'Capacity', 'Months', 'Start_Date', 'End_Date', 'Avg_CF']]\n",
    "der_cf['Capacity'] = der_cf['Capacity'].str.replace(',', '')       # Remove commas\n",
    "der_cf['Capacity'] = der_cf['Capacity'].str.extract(r'(\\d+)')      # Extract numeric part\n",
    "der_cf['Capacity'] = der_cf['Capacity'].astype(int)    \n",
    "der_cf.hist(column='Avg_CF', bins= 100)\n",
    "der_cf.to_csv(f'{tables}/Outputs/DER_CF.csv')\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d97e04c",
   "metadata": {},
   "source": [
    "### Merge OpenNY and Scrapped DER data by Addres"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "167b9c16",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SAT mean Avg_CF: 12.827641575893788, SAT count: 113\n",
      "FA mean Avg_CF: 11.981571848715436, FA count: 255\n"
     ]
    }
   ],
   "source": [
    "\n",
    "def normalize_address(addr):\n",
    "    addr = str(addr).lower()\n",
    "    addr = re.sub(r'[^\\w\\s]', '', addr)  # Remove punctuation\n",
    "    addr = re.sub(r'\\bst\\b', 'street', addr)\n",
    "    addr = re.sub(r'\\bave\\b', 'avenue', addr)\n",
    "    addr = re.sub(r'\\brd\\b', 'road', addr)\n",
    "    addr = re.sub(r'\\s+', ' ', addr)     # Remove extra whitespace\n",
    "    return addr.strip()\n",
    "\n",
    "# Normalize both address columns\n",
    "ders['clean_address'] = ders['Address'].apply(normalize_address)\n",
    "der_cf['clean_address'] = der_cf['Address'].apply(normalize_address)\n",
    "\n",
    "# Fuzzy matching function\n",
    "def match_address(addr, choices, scorer=fuzz.token_set_ratio, threshold=90):\n",
    "    match = process.extractOne(addr, choices, scorer=scorer)\n",
    "    if match and match[1] >= threshold:\n",
    "        return match[0]\n",
    "    return None\n",
    "\n",
    "# Create mapping from der_cf to ders\n",
    "der_cf['matched_address'] = der_cf['clean_address'].apply(\n",
    "    lambda x: match_address(x, ders['clean_address'].tolist())\n",
    ")\n",
    "\n",
    "# Optional: merge on matched cleaned address\n",
    "merged = pd.merge(der_cf, ders, left_on='matched_address', right_on='clean_address', suffixes=('_der_cf', '_ders'))\n",
    "\n",
    "merged['Capacity'] = pd.to_numeric(merged['Capacity'], errors='coerce')\n",
    "merged['Capacity kW DC'] = pd.to_numeric(merged['Capacity kW DC'], errors='coerce')\n",
    "\n",
    "# Drop rows where the difference exceeds 2\n",
    "merged = merged[abs(merged['Capacity'] - merged['Capacity kW DC']) <= 2]\n",
    "sat_count = (merged['Site Type'] == 'SAT').sum()\n",
    "fa_count = (merged['Site Type'] == 'FA').sum()\n",
    "sat_mean = merged.loc[merged['Site Type'] == 'SAT', 'Avg_CF'].mean()\n",
    "fa_mean = merged.loc[merged['Site Type'] == 'FA', 'Avg_CF'].mean()\n",
    "\n",
    "print(f\"SAT mean Avg_CF: {sat_mean}, SAT count: {sat_count}\")\n",
    "print(f\"FA mean Avg_CF: {fa_mean}, FA count: {fa_count}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c2ca790",
   "metadata": {},
   "source": [
    "### Summarizing Power Densities from manually deliniated fence lines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "a6e35317",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\twk54\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\pyogrio\\raw.py:196: UserWarning: Measured (M) geometry types are not supported. Original type 'Measured 3D MultiPolygon' is converted to 'MultiPolygon Z'\n",
      "  return ogr_read(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SAT Power density is 0.589, UT is 0.622, while Large sites had a power density of 0.503\n",
      "T-test results: t = -2.143, p = 0.033\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 550x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "font_properties = {\n",
    "    'family': 'Times New Roman',  # Choose font family (e.g., 'sans-serif', 'serif', 'monospace')\n",
    "    'style': 'normal',  # Choose font style (e.g., 'normal', 'italic', 'oblique')\n",
    "    'weight': 'normal',   # Choose font weight (e.g., 'normal', 'bold', 'light')\n",
    "    'size': 16          # Font size\n",
    "}\n",
    "supplemental = False\n",
    "\n",
    "output_folder = f'{root_dir}/Outputs/Figures'\n",
    "plot_name= 'Power_Densities'\n",
    "\n",
    "feature_class = 'Digitized_Fences'\n",
    "\n",
    "\n",
    "\n",
    "x_dim, y_dim = Two_x_dim, Two_y_dim\n",
    "\n",
    "    \n",
    "gdf = gpd.read_file(f'{gdb_path}', layer=feature_class)\n",
    "gdf = gdf.to_crs(epsg=6347)\n",
    "\n",
    "large = gdf[gdf['Site_Type'] == 'Large']\n",
    "SAT = gdf[gdf['Site_Type'] == 'SAT']\n",
    "UT = gdf[gdf['Site_Type'] == 'UT']\n",
    "\n",
    "# Calculate power density\n",
    "SAT_PD = SAT['Capacity_MW'] / (SAT['Shape_Area'] / 10000)\n",
    "UT_PD = UT['Capacity_MW'] / (UT['Shape_Area'] / 10000)\n",
    "large_PD = large['Capacity_MW'] / (large['Shape_Area'] / 10000)\n",
    "mean_sym = r'$\\bar{x}$'\n",
    "SAT_ac_per_MW = SAT['Capacity_MW'] / (SAT['Shape_Area'] * 0.000247105)\n",
    "UT_ac_per_MW = UT['Capacity_MW'] / (UT['Shape_Area'] * 0.000247105) \n",
    "Lg_ac_per_MW =large['Capacity_MW'] / (large['Shape_Area'] * 0.000247105)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "boxprops = dict(color=\"black\", linewidth=1.5, facecolor='gray')\n",
    "medianprops = dict(color=\"black\", linewidth=1.5)\n",
    "meanprops = dict(color=\"black\", linewidth=1.5, linestyle='--')  # Set mean line color to black\n",
    "\n",
    "# Prepare data for boxplot\n",
    "data = [large_PD, SAT_PD, UT_PD]\n",
    "labels = ['Large', 'Single Axis \\n Tracked', 'Fixed Axis']\n",
    "# Create boxplot\n",
    "fig, ax = plt.subplots(figsize=(x_dim, y_dim))\n",
    "boxplot = ax.boxplot(data, labels=labels, patch_artist=True, showmeans=True, meanline=True, boxprops=boxprops, medianprops=medianprops, meanprops=meanprops, widths=.7)\n",
    "\n",
    "# Set labels and title\n",
    "ax.set_ylabel('Power Density (MW$_{DC}$/ha)', fontdict=font_properties)\n",
    "\n",
    "# Set x-axis labels with font properties\n",
    "ax.set_xticklabels(labels, fontdict=font_properties)\n",
    "\n",
    "# Add mean and n values on the boxes\n",
    "offset = -0.05  # Adjust this value to move the labels down as needed\n",
    "\n",
    "# Create custom legend using the existing boxplot components\n",
    "handles = [boxplot['medians'][0], boxplot['means'][0]]\n",
    "labels = ['Median', 'Mean']\n",
    "# ax.legend(handles, labels, loc='upper right')\n",
    "plt.tight_layout()\n",
    "# Save the plot\n",
    "plt.savefig(f'{output_folder}\\{plot_name}.jpg', dpi=600, format='jpg')\n",
    "\n",
    "# Show the plot\n",
    "x_SAT_PD = round((SAT_PD.mean()), 3)\n",
    "x_UT_PD = round((UT_PD.mean()), 3)\n",
    "x_Large_PD = round((large_PD.mean()), 3)\n",
    "print(f'SAT Power density is {x_SAT_PD}, UT is {x_UT_PD}, while Large sites had a power density of {x_Large_PD}')\n",
    "SAT_PD_mean = SAT_PD.mean()\n",
    "UT_PD_mean = UT_PD.mean()\n",
    "invrtd_SAT_PD=1/SAT_PD_mean\n",
    "invrtd_UT_PD=1/UT_PD_mean\n",
    "from scipy.stats import ttest_ind, linregress\n",
    "# Perform a t-test between SAT_PD and UT_PD\n",
    "PD_t_stat, PD_p_value = stats.ttest_ind(SAT_PD, UT_PD)#, equal_var=False)\n",
    "\n",
    "print(f'T-test results: t = {PD_t_stat:.3f}, p = {PD_p_value:.3f}')\n",
    "\n",
    "plt.show()\n",
    "font_properties = {\n",
    "    'family': 'Times New Roman',  # Choose font family (e.g., 'sans-serif', 'serif', 'monospace')\n",
    "    'style': 'normal',  # Choose font style (e.g., 'normal', 'italic', 'oblique')\n",
    "    'weight': 'normal',   # Choose font weight (e.g., 'normal', 'bold', 'light')\n",
    "    'size': 12          # Font size\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91ae84da",
   "metadata": {},
   "source": [
    "### Power Density Over Time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "ad54d2bd",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\twk54\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\pyogrio\\raw.py:196: UserWarning: Measured (M) geometry types are not supported. Original type 'Measured 3D MultiPolygon' is converted to 'MultiPolygon Z'\n",
      "  return ogr_read(\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 550x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Single Axis Power Density Slope is 0.020445886. Y intercept is -40.7028\n",
      "Untracked Power Density Slope is 0.015288898 Y intercept is -30.2145\n",
      "Single Axis Tracking MW/Acres for 2020-2022 is:  0.059152345150259356\n",
      "Fixed Axis Tracking MW/Acres for 2020-2022 is:  0.07098941887300064\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\twk54\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\geopandas\\geodataframe.py:1819: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  super().__setitem__(key, value)\n",
      "c:\\Users\\twk54\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\geopandas\\geodataframe.py:1819: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  super().__setitem__(key, value)\n"
     ]
    }
   ],
   "source": [
    "from matplotlib.lines import Line2D\n",
    "\n",
    "font_properties = {\n",
    "    'family': 'Times New Roman',  # Choose font family (e.g., 'sans-serif', 'serif', 'monospace')\n",
    "    'style': 'normal',  # Choose font style (e.g., 'normal', 'italic', 'oblique')\n",
    "    'weight': 'normal',   # Choose font weight (e.g., 'normal', 'bold', 'light')\n",
    "    'size': 18          # Font size\n",
    "}\n",
    "\n",
    "\n",
    "plot_name = 'Power_Density_Over_Time'\n",
    "\n",
    "\n",
    "\n",
    "x_dim, y_dim =Two_x_dim, Two_y_dim\n",
    "\n",
    "feature_class = 'Digitized_Fences'\n",
    "gdf = gpd.read_file(f'{gdb_path}', layer=feature_class)\n",
    "gdf = gdf.to_crs(epsg=6347)\n",
    "SAT = gdf[gdf['Site_Type'] == 'SAT']\n",
    "UT = gdf[gdf['Site_Type'] == 'UT']\n",
    "\n",
    "fa_y = UT['Capacity_MW'] / (UT['Shape_Area']/10000)\n",
    "fa_x = UT['Application_Year']\n",
    "\n",
    "sa_y = SAT['Capacity_MW'] / (SAT['Shape_Area'] / 10000)\n",
    "sa_x = SAT['Application_Year']\n",
    "\n",
    "sa_n = sa_y.count()\n",
    "fa_n = fa_y.count()\n",
    "\n",
    "plt.subplots(figsize=(x_dim, y_dim))\n",
    "plt.scatter(fa_x, fa_y, color=C2_1[1], marker='o')\n",
    "fa_slope, fa_intercept, fa_r_value, fa_p_value, _ = linregress(fa_x, fa_y)\n",
    "fit_y_fa = fa_slope * fa_x + fa_intercept\n",
    "min_x_fa = fa_x.min()\n",
    "max_x_fa = fa_x.max()\n",
    "min_y_fa = fa_slope * min_x_fa + fa_intercept\n",
    "max_y_fa = fa_slope * max_x_fa + fa_intercept\n",
    "plt.plot([min_x_fa, max_x_fa], [min_y_fa, max_y_fa], color=C2_1[1], linestyle=\":\")\n",
    "\n",
    "plt.scatter(sa_x, sa_y, color=C2_1[0], marker='^')\n",
    "sa_slope, sa_intercept, sa_r_value, sa_p_value, _ = linregress(sa_x, sa_y)\n",
    "fit_y_sa = sa_slope * sa_x + sa_intercept\n",
    "plt.plot(sa_x, fit_y_sa, color=C2_1[0])\n",
    "\n",
    "plt.xlabel('Site Application Submission Year', fontdict=font_properties)\n",
    "plt.ylabel('Power Density (MW$_{\\mathrm{DC}}$/ha)', fontdict=font_properties)\n",
    "\n",
    "# Custom legend with marker and line styles\n",
    "legend_elements = [\n",
    "    Line2D([0], [0], color=C2_1[1], marker='o', linestyle=':', label='FA', markersize=8),\n",
    "    Line2D([0], [0], color=C2_1[0], marker='^', linestyle='-', label='SAT', markersize=8)\n",
    "]\n",
    "plt.legend(handles=legend_elements, prop=font_properties)\n",
    "\n",
    "plt.savefig(f'{figures}/{plot_name}.jpg', dpi=600, format='jpg')\n",
    "plt.show()\n",
    "\n",
    "fa_recent = UT[(UT['Application_Year'] > 2020) & (UT['Capacity_MW'] > 1)]\n",
    "sa_recent = SAT[(SAT['Application_Year'] >= 2018) & (SAT['Capacity_MW'] > 1)]\n",
    "sa_recent['MW per Ha'] = sa_recent['Capacity_MW'] / (sa_recent['Shape_Area'] / 1000)\n",
    "print(f'Single Axis Power Density Slope is {sa_slope:0.9f}. Y intercept is {sa_intercept:0.4f}')\n",
    "print(f'Untracked Power Density Slope is {fa_slope:0.9f} Y intercept is {fa_intercept:0.4f}')\n",
    "print(\"Single Axis Tracking MW/Acres for 2020-2022 is: \", sa_recent['MW per Ha'].mean())\n",
    "\n",
    "fa_recent['MW per Ha'] = fa_recent['Capacity_MW'] / (fa_recent['Shape_Area'] / 1000)\n",
    "print(\"Fixed Axis Tracking MW/Acres for 2020-2022 is: \", fa_recent['MW per Ha'].mean())\n",
    "\n",
    "font_properties = {\n",
    "    'family': 'Times New Roman',  # Choose font family (e.g., 'sans-serif', 'serif', 'monospace')\n",
    "    'style': 'normal',  # Choose font style (e.g., 'normal', 'italic', 'oblique')\n",
    "    'weight': 'normal',   # Choose font weight (e.g., 'normal', 'bold', 'light')\n",
    "    'size': 12          # Font size\n",
    "}\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be9bed36",
   "metadata": {},
   "source": [
    "### Distribution of Site-Transmission line distances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "dcc25d44",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\twk54\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\pyogrio\\raw.py:196: RuntimeWarning: driver GPKG does not support open option DRIVER\n",
      "  return ogr_read(\n"
     ]
    },
    {
     "data": {
      "image/png": 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5p2sBAABN4FaoP/DAA3r++edlGIan6wEAAG5y6/L7xx9/rK1bt2rjxo3q3LmzAgICXPrXrFnjkeIAAEDDuRXqERERuvnmmz1dCwAAaAK3Qn3FihWergMAADSRW/fUJens2bP68MMP9dJLL+nnn3+WJB0/flzFxcUeKw4AADScW6F+9OhRde3aVSNGjNCUKVP0448/SpIWLFigBx980GPFVVZWavbs2UpISFBISIguu+wyPfHEEy4T9AzD0Jw5cxQTE6OQkBAlJyfr0KFDHqsBAABf4VaoT506VUlJSfrpp58UEhLibL/55puVmZnpseIWLFig5cuX64UXXlBubq4WLFighQsXaunSpc5tFi5cqCVLlig9PV07d+5U69atNXDgQJWVlXmsDgAAfIFb99S3b9+uTz/9VIGBgS7tHTp00A8//OCRwiTp008/1YgRIzR06FDn+G+88YY+//xzSefO0hcvXqxHH31UI0aMkCStXLlS0dHRWrduHY+BBQBcUNw6U6+qqlJlZWW19u+//16hoaFNLuq8Pn36KDMzUwcPHpQk7d27Vx9//LEGDx4sSTpy5IgcDoeSk5OdrwkPD1evXr20Y8eOWsctLy9XUVGRywIAgK9zK9RvvPFGLV682Llus9lUXFysxx57zKNfHfvII49o9OjRSkxMVEBAgLp3765p06YpJSVFkuRwOCRJ0dHRLq+Ljo529tUkLS1N4eHhziUuLs5jNQMAYBa3Qv3ZZ5/VJ598oj/84Q8qKyvTXXfd5bz0vmDBAo8V99Zbb2nVqlVavXq1srOz9frrr+uZZ57R66+/3qRxZ82apcLCQudy7NgxD1UMAIB53Lqnfskll2jv3r1688039cUXX6i4uFgTJ05USkqKy8S5ppo5c6bzbF2SunbtqqNHjyotLU3jxo2T3W6XJOXn5ysmJsb5uvz8fF199dW1jhsUFKSgoCCP1QkAgDdwK9Qlyd/fX2PGjPFkLdX88ssv8vNzvZjQqlUr5/PbExISZLfblZmZ6QzxoqIi7dy5U/fee2+z1gYAgLdxK9RXrlxZZ//YsWPdKua3hg8frieffFLx8fHq3Lmz9uzZo0WLFmnChAmSzt3LnzZtmubNm6eOHTsqISFBs2fPVmxsrEaOHOmRGgAA8BVuhfrUqVNd1isqKvTLL78oMDBQF110kcdCfenSpZo9e7b+/ve/6+TJk4qNjdVf//pXzZkzx7nNQw89pJKSEk2ePFmnT59Wv379tGnTJgUHB3ukBgAAfIVbof7TTz9Vazt06JDuvfdezZw5s8lFnRcaGqrFixe7zLT/LZvNprlz52ru3Lke2y8AAL7I7e9+/62OHTvqqaeeqnYWDwAAWobHQl06N3nu+PHjnhwSAAA0kFuX39evX++ybhiGTpw4oRdeeEF9+/b1SGEAAKBx3Ar1384st9ls+t3vfqf+/fvr2Wef9URdAACgkdwK9fOfEwcAAN7Do/fUAQCAedw6U58xY0aDt120aJE7uwAAAI3kVqjv2bNHe/bsUUVFha688kpJ0sGDB9WqVSv16NHDuZ3NZvNMlQAAoF5uhfrw4cMVGhqq119/XW3atJF07gtp7rnnHv3pT3/SAw884NEiAQBA/dx+9GpaWpoz0CWpTZs2mjdvHrPfAQAwiVuhXlRUpB9//LFa+48//qiff/65yUUBAIDGcyvUb775Zt1zzz1as2aNvv/+e33//ff6n//5H02cOFG33HKLp2sEAAAN4NY99fT0dD344IO66667VFFRcW4gf39NnDhRTz/9tEcLtKrc3Nxa+9q1a6f4+PgWrAYAYAVuhfpFF12kF198UU8//bQOHz4sSbrsssvUunVrjxZnRYWFhbLZbBozZkyt24SEhGj//v0EOwCgUdwK9fNOnDihEydO6M9//rNCQkJkGAYfY6tHaWmpDMPQ+PHjZbfbq/U7HA5lZGSooKCAUAcANIpbof7vf/9bt99+u7Zu3SqbzaZDhw7p0ksv1cSJE9WmTRtmwDeA3W4ntAEAHuXWRLnp06crICBAeXl5uuiii5ztd9xxhzZt2uSx4gAAQMO5dab+wQcf6P3339cll1zi0t6xY0cdPXrUI4UBAIDGcetMvaSkxOUM/bxTp04pKCioyUUBAIDGcyvU//SnP2nlypXOdZvNpqqqKi1cuFA33HCDx4oDAAAN59bl94ULF2rAgAHavXu3zpw5o4ceekj79u3TqVOn9Mknn3i6RgAA0ABunal36dJFBw8eVL9+/TRixAiVlJTolltu0Z49e3TZZZd5ukYAANAAjT5Tr6io0KBBg5Senq5//vOfzVETAABwQ6PP1AMCAvTFF180Ry0AAKAJ3Lr8PmbMGL366quergUAADSBWxPlzp49q9dee00ffvihrrnmmmrf+b5o0SKPFAcAABquUaH+7bffqkOHDvrqq6/Uo0cPSdLBgwddtuG73wEAMEejQr1jx446ceKEtm7dKunc18IuWbJE0dHRzVIcAABouEbdUzcMw2V948aNKikp8WhBAADAPW5NlDvvtyEPAADM06hQt9ls1e6Zcw8dAADv0Kh76oZhaPz48c6HtpSVlelvf/tbtdnva9as8VyFAACgQRoV6uPGjXNZHzNmjEeLAQAA7mtUqK9YsaK56gAAAE3UpIlyAADAexDqAABYBKEOAIBFEOoAAFiE14f6Dz/8oDFjxqht27YKCQlR165dtXv3bme/YRiaM2eOYmJiFBISouTkZB06dMjEigEAMIdXh/pPP/2kvn37KiAgQBs3btTXX3+tZ599Vm3atHFus3DhQi1ZskTp6enauXOnWrdurYEDB6qsrMzEygEAaHluPXq1pSxYsEBxcXEuH6VLSEhw/rdhGFq8eLEeffRRjRgxQpK0cuVKRUdHa926dRo9enSL1wwAgFm8+kx9/fr1SkpK0m233aaoqCh1795dr7zyirP/yJEjcjgcSk5OdraFh4erV69e2rFjR63jlpeXq6ioyGUBAMDXeXWof/vtt1q+fLk6duyo999/X/fee6/uv/9+vf7665Ikh8MhSdUe/RodHe3sq0laWprCw8OdS1xcXPMdBAAALcSrQ72qqko9evTQ/Pnz1b17d02ePFmTJk1Senp6k8adNWuWCgsLncuxY8c8VDEAAObx6lCPiYnRH/7wB5e2Tp06KS8vT5Jkt9slSfn5+S7b5OfnO/tqEhQUpLCwMJcFAABf59Wh3rdvXx04cMCl7eDBg2rfvr2kc5Pm7Ha7MjMznf1FRUXauXOnevfu3aK1AgBgNq+e/T59+nT16dNH8+fP1+23367PP/9cL7/8sl5++WVJ557lPm3aNM2bN08dO3ZUQkKCZs+erdjYWI0cOdLc4gEAaGFeHeo9e/bU2rVrNWvWLM2dO1cJCQlavHixUlJSnNs89NBDKikp0eTJk3X69Gn169dPmzZtUnBwsImVAwDQ8rw61CVp2LBhGjZsWK39NptNc+fO1dy5c1uwKgAAvI9X31MHAAANR6gDAGARhDoAABZBqAMAYBGEOgAAFkGoAwBgEYQ6AAAWQagDAGARhDoAABZBqAMAYBGEOgAAFkGoAwBgEYQ6AAAWQagDAGARhDoAABZBqAMAYBGEOgAAFkGoAwBgEYQ6AAAWQagDAGARhDoAABZBqAMAYBGEOgAAFkGoAwBgEYQ6AAAWQagDAGARhDoAABZBqAMAYBGEOgAAFkGoAwBgEYQ6AAAWQagDAGARhDoAABZBqAMAYBGEOgAAFkGoAwBgEYQ6AAAWQagDAGARPhXqTz31lGw2m6ZNm+ZsKysr05QpU9S2bVtdfPHFGjVqlPLz880rEgAAk/ibXUBD7dq1Sy+99JKuuuoql/bp06frvffe09tvv63w8HClpqbqlltu0SeffGJSpZ6Rm5tbY3u7du0UHx/fwtUAAHyBT4R6cXGxUlJS9Morr2jevHnO9sLCQr366qtavXq1+vfvL0lasWKFOnXqpM8++0x//OMfzSrZbYWFhbLZbBozZkyN/SEhIdq/fz/BDgCoxidCfcqUKRo6dKiSk5NdQj0rK0sVFRVKTk52tiUmJio+Pl47duyoNdTLy8tVXl7uXC8qKmq+4huptLRUhmFo/PjxstvtLn0Oh0MZGRkqKCgg1AEA1Xh9qL/55pvKzs7Wrl27qvU5HA4FBgYqIiLCpT06OloOh6PWMdPS0vT44497ulSPstvtBDcAoFG8eqLcsWPHNHXqVK1atUrBwcEeG3fWrFkqLCx0LseOHfPY2AAAmMWrQz0rK0snT55Ujx495O/vL39/f3300UdasmSJ/P39FR0drTNnzuj06dMur8vPz6926frXgoKCFBYW5rIAAODrvPry+4ABA/Tll1+6tN1zzz1KTEzUww8/rLi4OAUEBCgzM1OjRo2SJB04cEB5eXnq3bu3GSUDAGAarw710NBQdenSxaWtdevWatu2rbN94sSJmjFjhiIjIxUWFqb77rtPvXv39smZ7wAANIVXh3pDPPfcc/Lz89OoUaNUXl6ugQMH6sUXXzS7LAAAWpzPhfr//d//uawHBwdr2bJlWrZsmTkFAQDgJbx6ohwAAGg4Qh0AAIsg1AEAsAhCHQAAiyDUAQCwCEIdAACLINQBALAIn/ucOuqWl5engoKCWvvbtWvH098AwKIIdQvJy8tTYmKiSktLa90mJCRE+/fvJ9gBwIIIdR+Um5tba3tpaanGjx9f41PqHA6HMjIyVFBQQKgDgAUR6j6ksLBQNptNY8aMqXM7u91OaAPABYhQ9yGlpaUyDKPWM/F9+/bp3XffNaEyAIA3INR9UG1n4g6Hw4RqAADego+0AQBgEYQ6AAAWQagDAGARhDoAABZBqAMAYBGEOgAAFkGoAwBgEYQ6AAAWQagDAGARhDoAABZBqAMAYBGEOgAAFkGoAwBgEYQ6AAAWQagDAGARhDoAABbhb3YB8C55eXkqKCiosa9du3aKj49v4YoAAA1FqMMpLy9PiYmJKi0trbE/JCRE+/fvJ9gBwEsR6nAqKChQaWmpxo8fL7vd7tLncDiUkZGhgoICQh0AvBShjmrsdjvBDQA+iIlyAABYBGfqF6Dc3NxGtQMAfAOhfgEpLCyUzWbTmDFjzC4FANAMCPULSGlpqQzDqHEinCTt27dP7777rgmVAQA8gVC/ANU2Ec7hcJhQDQDAU7x6olxaWpp69uyp0NBQRUVFaeTIkTpw4IDLNmVlZZoyZYratm2riy++WKNGjVJ+fr5JFVtfbm6usrOza1zy8vLMLg8ALmhefab+0UcfacqUKerZs6fOnj2rf/zjH7rxxhv19ddfq3Xr1pKk6dOn67333tPbb7+t8PBwpaam6pZbbtEnn3xicvXW0pD78Xw5DQCYy6tDfdOmTS7rGRkZioqKUlZWlv785z+rsLBQr776qlavXq3+/ftLklasWKFOnTrps88+0x//+Eczyrak+u7H8+U0AGA+rw713yosLJQkRUZGSpKysrJUUVGh5ORk5zaJiYmKj4/Xjh07ag318vJylZeXO9eLioqasWpr4YtpAMB7efU99V+rqqrStGnT1LdvX3Xp0kXSubPDwMBARUREuGwbHR1d56SvtLQ0hYeHO5e4uLjmLB0AgBbhM6E+ZcoUffXVV3rzzTebPNasWbNUWFjoXI4dO+aBCgEAMJdPXH5PTU3Vhg0btG3bNl1yySXOdrvdrjNnzuj06dMuZ+v5+fk13vc9LygoSEFBQc1ZMgAALc6rz9QNw1BqaqrWrl2rLVu2KCEhwaX/mmuuUUBAgDIzM51tBw4cUF5ennr37t3S5QIAYCqvPlOfMmWKVq9erXfeeUehoaHO++Th4eEKCQlReHi4Jk6cqBkzZigyMlJhYWG677771Lt3b2a++6C8vDwVFBTU2NeuXTsm6AFAPbw61JcvXy5Juv76613aV6xYofHjx0uSnnvuOfn5+WnUqFEqLy/XwIED9eKLL7ZwpWiqvLw8JSYmqrS0tMZ+PgMPAPXz6lA3DKPebYKDg7Vs2TItW7asBSpCcykoKFBpaWmNn4PnM/AA0DBeHeq48PA5eABwn1dPlAMAAA3HmTpaTF0T4XJzc1u4GgCwHkIdLaK+iXAAgKYj1NEi6poIJ0n79u3Tu+++a0JlAGAdhDpaVG0T4er6rn4AQMMwUQ4AAIsg1AEAsAhCHQAAiyDUAQCwCEIdAACLINQBALAIQh0AAIsg1AEAsAhCHQAAiyDUAQCwCEIdAACLINQBALAIQh0AAIsg1AEAsAhCHQAAi+B56vAZubm5tfa1a9euxue0N1ReXp4KCgqaZWwAaCmEOrxeYWGhbDabxowZU+s2ISEh2r9/v1vhm5eXp8TERJWWlnp8bABoSYQ6vF5paakMw9D48eNlt9ur9TscDmVkZKigoMCt4C0oKFBpaWmN4zd1bABoSYQ6PKq2S+R1XTpvKLvd3qzB2tzjA0BzI9ThEQ25RA4AaF6EOjyivkvk+/bt07vvvtusNTTnRLq61DXJrrn3DQC/RqjDo2q7hO1wOJptn809ka4u9U2ya859A8BvEerwec09ka4udU2ya+59A8BvEeqwDDMnutW377puDZSXlysoKKjGPi7dA2gMQh1oRg25NWCz2WQYRo19XLoH0BiEOtAA7n5Ur6ETCN39jDyT9GrGzwUXKkIdqIOnPqpX3wRCd24dMEmvZvxccCEj1HHBcOds2xs+qlcbX5+k11zft+/rPxegKQh1WJ4nzrbN+KheQ/niN+G1xPft++LPBWgqQh2W581n2xcqvm8faB6EOi4Y3ny2XZfm/D59s7l7Nl3XpXuzfy7NOUmPCYCoj2VCfdmyZXr66aflcDjUrVs3LV26VNdee63ZZQFu4/v0a9aQiXBmac5JekwARENYItT/9a9/acaMGUpPT1evXr20ePFiDRw4UAcOHFBUVJTZ5QFu8dRtA3e/+KauPqn5zwrrukJR10S4pv5cmnLcDZ2kt337dnXq1KlR+67vuJv6Ecjm/n0318TI+sb2xPjeuu+aWCLUFy1apEmTJumee+6RJKWnp+u9997Ta6+9pkceecTk6oCmcfe2QVO/+KauPqn5zgobeoWiuX4unjju2mpr6r7rGrs+9Z3pN+fvuzknRpp5BcMbr574fKifOXNGWVlZmjVrlrPNz89PycnJ2rFjh4mVAeZqyhff1NUnNe9ktuae2FjX+M193E3Zd1OPu66rCM193M05MdLMjzB648cnfT7UCwoKVFlZqejoaJf26Oho7d+/v8bXlJeXq7y83LleWFgoSSoqKmpyPcXFxZLO/QX3632cd/4swp3+pryWfV+4+z5z5kyN/RUVFbX219V3vl2SsrKynP/mf8vPz09VVVU19h04cKDW2htad3P8XJp63HUdV1P3Xd9x5+fnN6g2M3/fNY3fnGM3ZPy6xvbUvouLi5ucL+dfX9+VHBk+7ocffjAkGZ9++qlL+8yZM41rr722xtc89thjhiQWFhYWFhafWo4dO1ZnJvr8mXq7du3UqlUr51+p5+Xn59d4OUSSZs2apRkzZjjXq6qqdOrUKbVt21Y2m61J9RQVFSkuLk7Hjh1TWFhYk8byFhyTb+CYfIcVj4tjal6GYejnn39WbGxsndv5fKgHBgbqmmuuUWZmpkaOHCnpXEhnZmYqNTW1xtcEBQVVm+UZERHh0brCwsJM/0fgaRyTb+CYfIcVj4tjaj7h4eH1buPzoS5JM2bM0Lhx45SUlKRrr71WixcvVklJiXM2PAAAFwJLhPodd9yhH3/8UXPmzJHD4dDVV1+tTZs2VZs8BwCAlVki1CUpNTW11svtLSkoKEiPPfZYnV/i4Gs4Jt/AMfkOKx4Xx+QdbIZR3/x4AADgC/zMLgAAAHgGoQ4AgEUQ6gAAWAShDgCARRDqHrZs2TJ16NBBwcHB6tWrlz7//HOzS3JbWlqaevbsqdDQUEVFRWnkyJHO7zq2iqeeeko2m03Tpk0zu5Qm+eGHHzRmzBi1bdtWISEh6tq1q3bv3m12WW6rrKzU7NmzlZCQoJCQEF122WV64okn6v/eay+ybds2DR8+XLGxsbLZbFq3bp1Lv2EYmjNnjmJiYhQSEqLk5GQdOnTInGIbqK5jqqio0MMPP6yuXbuqdevWio2N1dixY3X8+HHzCm6A+n5Pv/a3v/1NNptNixcvbrH6GotQ96Dzz3V/7LHHlJ2drW7dumngwIE6efKk2aW55aOPPtKUKVP02WefafPmzaqoqNCNN96okpISs0vziF27dumll17SVVddZXYpTfLTTz+pb9++CggI0MaNG/X111/r2WefVZs2bcwuzW0LFizQ8uXL9cILLyg3N1cLFizQwoULtXTpUrNLa7CSkhJ169ZNy5Ytq7F/4cKFWrJkidLT07Vz5061bt1aAwcOVFlZWQtX2nB1HdMvv/yi7OxszZ49W9nZ2VqzZo0OHDigm266yYRKG66+39N5a9eu1WeffVbv17SazhMPVcE51157rTFlyhTnemVlpREbG2ukpaWZWJXnnDx50pBkfPTRR2aX0mQ///yz0bFjR2Pz5s3GddddZ0ydOtXsktz28MMPG/369TO7DI8aOnSoMWHCBJe2W265xUhJSTGpoqaRZKxdu9a5XlVVZdjtduPpp592tp0+fdoICgoy3njjDRMqbLzfHlNNPv/8c0OScfTo0ZYpqolqO6bvv//e+P3vf2989dVXRvv27Y3nnnuuxWtrKM7UPeT8c92Tk5OdbVZ7rvv5R9RGRkaaXEnTTZkyRUOHDnX5ffmq9evXKykpSbfddpuioqLUvXt3vfLKK2aX1SR9+vRRZmamDh48KEnau3evPv74Yw0ePNjkyjzjyJEjcjgcLv/+wsPD1atXL8u8X0jn3jNsNpvHn63RkqqqqnT33Xdr5syZ6ty5s9nl1Msy3yhnNnee6+5LqqqqNG3aNPXt21ddunQxu5wmefPNN5Wdna1du3aZXYpHfPvtt1q+fLlmzJihf/zjH9q1a5fuv/9+BQYGaty4cWaX55ZHHnlERUVFSkxMVKtWrVRZWaknn3xSKSkpZpfmEeefq17T+8X5Pl9XVlamhx9+WHfeeadXPAzFXQsWLJC/v7/uv/9+s0tpEEIdDTJlyhR99dVX+vjjj80upUmOHTumqVOnavPmzQoODja7HI+oqqpSUlKS5s+fL0nq3r27vvrqK6Wnp/tsqL/11ltatWqVVq9erc6dOysnJ0fTpk1TbGyszx7ThaSiokK33367DMPQ8uXLzS7HbVlZWXr++eeVnZ3d5MdytxQuv3uIO8919xWpqanasGGDtm7dqksuucTscpokKytLJ0+eVI8ePeTv7y9/f3999NFHWrJkifz9/VVZWWl2iY0WExOjP/zhDy5tnTp1Ul5enkkVNd3MmTP1yCOPaPTo0eratavuvvtuTZ8+XWlpaWaX5hHn3xOs+H5xPtCPHj2qzZs3+/RZ+vbt23Xy5EnFx8c73y+OHj2qBx54QB06dDC7vBoR6h7y6+e6n3f+ue69e/c2sTL3GYah1NRUrV27Vlu2bFFCQoLZJTXZgAED9OWXXyonJ8e5JCUlKSUlRTk5OWrVqpXZJTZa3759q33U8ODBg2rfvr1JFTXdL7/8Ij8/17enVq1aqaqqyqSKPCshIUF2u93l/aKoqEg7d+702fcL6f8H+qFDh/Thhx+qbdu2ZpfUJHfffbe++OILl/eL2NhYzZw5U++//77Z5dWIy+8eZLXnuk+ZMkWrV6/WO++8o9DQUOe9vvDwcIWEhJhcnXtCQ0OrzQlo3bq12rZt67NzBaZPn64+ffpo/vz5uv322/X555/r5Zdf1ssvv2x2aW4bPny4nnzyScXHx6tz587as2ePFi1apAkTJphdWoMVFxfrm2++ca4fOXJEOTk5ioyMVHx8vKZNm6Z58+apY8eOSkhI0OzZsxUbG6uRI0eaV3Q96jqmmJgY3XrrrcrOztaGDRtUWVnpfM+IjIxUYGCgWWXXqb7f02//MAkICJDdbteVV17Z0qU2jNnT761m6dKlRnx8vBEYGGhce+21xmeffWZ2SW6TVOOyYsUKs0vzKF//SJthGMa7775rdOnSxQgKCjISExONl19+2eySmqSoqMiYOnWqER8fbwQHBxuXXnqp8c9//tMoLy83u7QG27p1a43//4wbN84wjHMfa5s9e7YRHR1tBAUFGQMGDDAOHDhgbtH1qOuYjhw5Uut7xtatW80uvVb1/Z5+y9s/0sajVwEAsAjuqQMAYBGEOgAAFkGoAwBgEYQ6AAAWQagDAGARhDoAABZBqAMAYBGEOgAAFkGoAxZgs9m0bt06s8toFmfOnNHll1+uTz/9VJL03XffyWazKScnx6P7SU9P1/Dhwz06JtDSCHXAS40fP142m002m00BAQGKjo7WX/7yF7322mvVHmxy4sQJDR48uEHj+tofAOnp6UpISFCfPn2adT8TJkxQdna2tm/f3qz7AZoToQ54sUGDBunEiRP67rvvtHHjRt1www2aOnWqhg0bprNnzzq3s9vtCgoKMrHS5mEYhl544QVNnDix2fcVGBiou+66S0uWLGn2fQHNhVAHvFhQUJDsdrt+//vfq0ePHvrHP/6hd955Rxs3blRGRoZzu1+ffZ85c0apqamKiYlRcHCw2rdv73wO+flnQN98882y2WzO9cOHD2vEiBGKjo7WxRdfrJ49e+rDDz90qaVDhw6aP3++JkyYoNDQUMXHx1d7Etz333+vO++8U5GRkWrdurWSkpK0c+dOZ/8777yjHj16KDg4WJdeeqkef/xxlz9OfisrK0uHDx/W0KFDa92msrJSEyZMUGJiovMZ8jabTS+99JKGDRumiy66SJ06ddKOHTv0zTff6Prrr1fr1q3Vp08fHT582GWs4cOHa/369SotLa11f4A3I9QBH9O/f39169ZNa9asqbF/yZIlWr9+vd566y0dOHBAq1atcob3rl27JEkrVqzQiRMnnOvFxcUaMmSIMjMztWfPHg0aNEjDhw93huR5zz77rJKSkrRnzx79/e9/17333ut8lntxcbGuu+46/fDDD1q/fr327t2rhx56yHmrYPv27Ro7dqymTp2qr7/+Wi+99JIyMjL05JNP1nqs27dv1xVXXKHQ0NAa+8vLy3XbbbcpJydH27dvV3x8vLPviSee0NixY5WTk6PExETddddd+utf/6pZs2Zp9+7dMgxDqampLuMlJSXp7NmzLn+IAD7F3IfEAajNuHHjjBEjRtTYd8cddxidOnVyrksy1q5daxiGYdx3331G//79jaqqqhpf++tt69K5c2dj6dKlzvX27dsbY8aMca5XVVUZUVFRxvLlyw3DMIyXXnrJCA0NNf7973/XON6AAQOM+fPnu7T913/9lxETE1NrDVOnTjX69+/v0nb+EZ/bt283BgwYYPTr1884ffp0tWN89NFHnes7duwwJBmvvvqqs+2NN94wgoODq+2zTZs2RkZGRq01Ad6MM3XABxmGIZvNVmPf+PHjlZOToyuvvFL333+/Pvjgg3rHKy4u1oMPPqhOnTopIiJCF198sXJzc6udqV911VXO/7bZbLLb7Tp58qQkKScnR927d1dkZGSN+9i7d6/mzp2riy++2LlMmjRJJ06c0C+//FLja0pLSxUcHFxj35133qmSkhJ98MEHCg8Pr9b/61qjo6MlSV27dnVpKysrU1FRkcvrQkJCaq0H8HaEOuCDcnNzlZCQUGNfjx49dOTIET3xxBMqLS3V7bffrltvvbXO8R588EGtXbtW8+fP1/bt25WTk6OuXbvqzJkzLtsFBAS4rNtsNufl9ZCQkDr3UVxcrMcff1w5OTnO5csvv9ShQ4dqDe527drpp59+qrFvyJAh+uKLL7Rjx44a+39d6/k/gGpq++0nCU6dOqXf/e53dR4L4K38zS4AQONs2bJFX375paZPn17rNmFhYbrjjjt0xx136NZbb9WgQYN06tQpRUZGKiAgQJWVlS7bf/LJJxo/frxuvvlmSecC+LvvvmtUXVdddZX+8z//07mf3+rRo4cOHDigyy+/vMFjdu/eXcuXL6/xysS9996rLl266KabbtJ7772n6667rlH11uTw4cMqKytT9+7dmzwWYAZCHfBi5eXlcjgcqqysVH5+vjZt2qS0tDQNGzZMY8eOrfE1ixYtUkxMjLp37y4/Pz+9/fbbstvtioiIkHRuFntmZqb69u2roKAgtWnTRh07dtSaNWs0fPhw2Ww2zZ49u9oZbH3uvPNOzZ8/XyNHjlRaWppiYmK0Z88excbGqnfv3pozZ46GDRum+Ph43XrrrfLz89PevXv11Vdfad68eTWOecMNN6i4uFj79u1Tly5dqvXfd999qqys1LBhw7Rx40b169evUTX/1vbt23XppZfqsssua9I4gFm4/A54sU2bNikmJkYdOnTQoEGDtHXrVi1ZskTvvPOOWrVqVeNrQkNDtXDhQiUlJalnz5767rvv9L//+7/y8zv3v/uzzz6rzZs3Ky4uznlGumjRIrVp00Z9+vTR8OHDNXDgQPXo0aNRtQYGBuqDDz5QVFSUhgwZoq5du+qpp55y1jlw4EBt2LBBH3zwgXr27Kk//vGPeu6559S+fftax2zbtq1uvvlmrVq1qtZtpk2bpscff1xDhgxxfuucu9544w1NmjSpSWMAZrIZhmGYXQQA1OaLL77QX/7yFx0+fFgXX3xxs+1n37596t+/vw4ePFjjxDvAF3CmDsCrXXXVVVqwYIGOHDnSrPs5ceKEVq5cSaDDp3GmDgCARXCmDgCARRDqAABYBKEOAIBFEOoAAFgEoQ4AgEUQ6gAAWAShDgCARRDqAABYBKEOAIBF/D+rVDyh+aZB4gAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 550x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_title = 'Distance to Transmission Line Distribution'\n",
    "feature_class = 'Detected_Sites'\n",
    "sites = gpd.read_file(f'{gdb_path}', layer=feature_class, driver = 'OpenFileGDB')\n",
    "tl='transmission_lines'\n",
    "transmission_lines = gpd.read_file(gdb_path, layer = tl)\n",
    "plot_name = 'Detected_Sites_Distance_to_Transmission'\n",
    "distances = (sites.geometry.apply(lambda geom: transmission_lines.distance(geom).min()))/1000\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(x_dim, y_dim))\n",
    "ax.hist(distances, bins = 50, color= Gray3, edgecolor='black')\n",
    "plt.xlabel('Distance (km)')\n",
    "plt.ylabel('Frequency')\n",
    "plt.savefig(f'{figures}\\{plot_name}.jpg', dpi=300, format='jpg')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8981785d",
   "metadata": {},
   "source": [
    "### Simulate DC:AC Losses\n",
    "1. Data files are downloaded using DCAC_download.py. run that script before attempting this code block\n",
    "2. Raw data are trimmed to full years\n",
    "3. Trimmed data are used to simulate losses at different ratios\n",
    "4. Losses are graphed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "cc629093",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trimmed data (10).csv to include only complete years from 2019 to 2023.\n",
      "Trimmed data (100).csv to include only complete years from 2018 to 2024.\n",
      "Trimmed data (11).csv to include only complete years from 2019 to 2024.\n",
      "Trimmed data (12).csv to include only complete years from 2019 to 2024.\n",
      "Trimmed data (13).csv to include only complete years from 2022 to 2024.\n",
      "Trimmed data (14).csv to include only complete years from 2022 to 2024.\n",
      "Trimmed data (15).csv to include only complete years from 2018 to 2022.\n",
      "Trimmed data (16).csv to include only complete years from 2018 to 2022.\n",
      "Trimmed data (17).csv to include only complete years from 2018 to 2022.\n",
      "Trimmed data (18).csv to include only complete years from 2018 to 2022.\n",
      "Trimmed data (19).csv to include only complete years from 2018 to 2022.\n",
      "Trimmed data (20).csv to include only complete years from 2022 to 2024.\n",
      "Skipping data (21).csv: not enough data for a complete year.\n",
      "Skipping data (22).csv: not enough data for a complete year.\n",
      "Trimmed data (23).csv to include only complete years from 2019 to 2024.\n",
      "Trimmed data (24).csv to include only complete years from 2022 to 2024.\n",
      "Trimmed data (25).csv to include only complete years from 2019 to 2024.\n",
      "Trimmed data (26).csv to include only complete years from 2019 to 2024.\n",
      "Trimmed data (27).csv to include only complete years from 2019 to 2024.\n",
      "Skipping data (28).csv: not enough data for a complete year.\n",
      "Skipping data (29).csv: not enough data for a complete year.\n",
      "Trimmed data (3).csv to include only complete years from 2019 to 2024.\n",
      "Trimmed data (30).csv to include only complete years from 2022 to 2024.\n",
      "Trimmed data (31).csv to include only complete years from 2019 to 2024.\n",
      "Trimmed data (32).csv to include only complete years from 2022 to 2024.\n",
      "Skipping data (33).csv: not enough data for a complete year.\n",
      "Skipping data (34).csv: not enough data for a complete year.\n",
      "Trimmed data (35).csv to include only complete years from 2018 to 2023.\n",
      "Trimmed data (36).csv to include only complete years from 2021 to 2024.\n",
      "Trimmed data (37).csv to include only complete years from 2022 to 2024.\n",
      "Skipping data (38).csv: not enough data for a complete year.\n",
      "Trimmed data (39).csv to include only complete years from 2019 to 2022.\n",
      "Trimmed data (4).csv to include only complete years from 2022 to 2024.\n",
      "Trimmed data (40).csv to include only complete years from 2020 to 2024.\n",
      "Trimmed data (41).csv to include only complete years from 2018 to 2020.\n",
      "Trimmed data (42).csv to include only complete years from 2020 to 2024.\n",
      "Skipping data (43).csv: not enough data for a complete year.\n",
      "Skipping data (44).csv: not enough data for a complete year.\n",
      "Skipping data (45).csv: not enough data for a complete year.\n",
      "Trimmed data (46).csv to include only complete years from 2020 to 2024.\n",
      "Trimmed data (47).csv to include only complete years from 2016 to 2020.\n",
      "Trimmed data (48).csv to include only complete years from 2018 to 2024.\n",
      "Skipping data (49).csv: not enough data for a complete year.\n",
      "Trimmed data (5).csv to include only complete years from 2018 to 2022.\n",
      "Trimmed data (50).csv to include only complete years from 2018 to 2024.\n",
      "Trimmed data (51).csv to include only complete years from 2014 to 2024.\n",
      "Skipping data (52).csv: not enough data for a complete year.\n",
      "Trimmed data (53).csv to include only complete years from 2016 to 2024.\n",
      "Trimmed data (54).csv to include only complete years from 2016 to 2024.\n",
      "Trimmed data (55).csv to include only complete years from 2021 to 2023.\n",
      "Trimmed data (56).csv to include only complete years from 2021 to 2024.\n",
      "Trimmed data (57).csv to include only complete years from 2022 to 2024.\n",
      "Trimmed data (58).csv to include only complete years from 2019 to 2024.\n",
      "Trimmed data (59).csv to include only complete years from 2020 to 2024.\n",
      "Trimmed data (6).csv to include only complete years from 2022 to 2024.\n",
      "Trimmed data (60).csv to include only complete years from 2020 to 2024.\n",
      "Trimmed data (61).csv to include only complete years from 2021 to 2024.\n",
      "Trimmed data (62).csv to include only complete years from 2021 to 2024.\n",
      "Trimmed data (63).csv to include only complete years from 2022 to 2024.\n",
      "Trimmed data (64).csv to include only complete years from 2019 to 2024.\n",
      "Trimmed data (65).csv to include only complete years from 2022 to 2024.\n",
      "Trimmed data (66).csv to include only complete years from 2021 to 2024.\n",
      "Trimmed data (67).csv to include only complete years from 2021 to 2024.\n",
      "Trimmed data (68).csv to include only complete years from 2022 to 2024.\n",
      "Trimmed data (69).csv to include only complete years from 2021 to 2024.\n",
      "Trimmed data (7).csv to include only complete years from 2018 to 2022.\n",
      "Trimmed data (70).csv to include only complete years from 2020 to 2024.\n",
      "Trimmed data (71).csv to include only complete years from 2020 to 2024.\n",
      "Trimmed data (72).csv to include only complete years from 2020 to 2024.\n",
      "Trimmed data (73).csv to include only complete years from 2020 to 2024.\n",
      "Trimmed data (74).csv to include only complete years from 2022 to 2024.\n",
      "Trimmed data (75).csv to include only complete years from 2022 to 2024.\n",
      "Trimmed data (76).csv to include only complete years from 2022 to 2024.\n",
      "Trimmed data (77).csv to include only complete years from 2021 to 2024.\n",
      "Trimmed data (78).csv to include only complete years from 2021 to 2024.\n",
      "Trimmed data (79).csv to include only complete years from 2021 to 2024.\n",
      "Trimmed data (8).csv to include only complete years from 2018 to 2022.\n",
      "Trimmed data (80).csv to include only complete years from 2021 to 2024.\n",
      "Trimmed data (81).csv to include only complete years from 2022 to 2024.\n",
      "Trimmed data (82).csv to include only complete years from 2022 to 2024.\n",
      "Trimmed data (83).csv to include only complete years from 2022 to 2024.\n",
      "Trimmed data (84).csv to include only complete years from 2022 to 2024.\n",
      "Skipping data (85).csv: not enough data for a complete year.\n",
      "Trimmed data (86).csv to include only complete years from 2019 to 2024.\n",
      "Trimmed data (87).csv to include only complete years from 2020 to 2024.\n",
      "Trimmed data (88).csv to include only complete years from 2022 to 2024.\n",
      "Trimmed data (89).csv to include only complete years from 2022 to 2024.\n",
      "Trimmed data (9).csv to include only complete years from 2022 to 2024.\n",
      "Trimmed data (90).csv to include only complete years from 2022 to 2024.\n",
      "Trimmed data (91).csv to include only complete years from 2018 to 2024.\n",
      "Trimmed data (92).csv to include only complete years from 2018 to 2024.\n",
      "Trimmed data (93).csv to include only complete years from 2018 to 2024.\n",
      "Skipping data (94).csv: not enough data for a complete year.\n",
      "Skipping data (95).csv: not enough data for a complete year.\n",
      "Trimmed data (96).csv to include only complete years from 2020 to 2024.\n",
      "Skipping data (97).csv: not enough data for a complete year.\n",
      "Skipping data (98).csv: not enough data for a complete year.\n",
      "Skipping data (99).csv: not enough data for a complete year.\n",
      "Trimmed data - 2024-05-20T123910.078.csv to include only complete years from 2016 to 2024.\n",
      "Trimmed data - 2024-05-20T123934.342.csv to include only complete years from 2017 to 2024.\n",
      "Trimmed data - 2024-05-20T123954.525.csv to include only complete years from 2018 to 2022.\n",
      "Skipping data - 2024-05-20T124008.706.csv: not enough data for a complete year.\n",
      "Skipping data - 2024-05-20T124021.562.csv: not enough data for a complete year.\n",
      "Trimmed data - 2024-05-20T124040.880.csv to include only complete years from 2020 to 2023.\n",
      "Trimmed data - 2024-05-20T124103.042.csv to include only complete years from 2018 to 2024.\n",
      "Trimmed data - 2024-05-20T124123.470.csv to include only complete years from 2020 to 2024.\n",
      "Skipping data - 2024-05-20T124137.356.csv: not enough data for a complete year.\n",
      "Trimmed data - 2024-05-20T124155.955.csv to include only complete years from 2020 to 2023.\n",
      "Trimmed data - 2024-05-20T124215.402.csv to include only complete years from 2021 to 2024.\n",
      "Trimmed data - 2024-05-20T124233.600.csv to include only complete years from 2019 to 2022.\n",
      "Trimmed data - 2024-05-20T124249.927.csv to include only complete years from 2022 to 2024.\n",
      "Trimmed data - 2024-05-20T124307.054.csv to include only complete years from 2022 to 2024.\n",
      "Trimmed data - 2024-05-20T124327.264.csv to include only complete years from 2020 to 2024.\n",
      "Trimmed data - 2024-05-20T124350.130.csv to include only complete years from 2019 to 2024.\n",
      "Trimmed data - 2024-05-20T124406.226.csv to include only complete years from 2018 to 2024.\n",
      "Trimmed data - 2024-05-20T124428.991.csv to include only complete years from 2017 to 2023.\n",
      "Trimmed data - 2024-05-20T124450.642.csv to include only complete years from 2017 to 2023.\n",
      "Trimmed data - 2024-05-20T124515.158.csv to include only complete years from 2017 to 2024.\n",
      "Trimmed data - 2024-05-20T124539.920.csv to include only complete years from 2018 to 2024.\n",
      "Trimmed data - 2024-05-20T124556.032.csv to include only complete years from 2021 to 2023.\n",
      "Trimmed data - 2024-05-20T124614.409.csv to include only complete years from 2018 to 2022.\n",
      "Trimmed data - 2024-05-20T124637.700.csv to include only complete years from 2018 to 2024.\n",
      "Trimmed data - 2024-05-20T124700.990.csv to include only complete years from 2018 to 2024.\n",
      "Trimmed data - 2024-05-20T124717.326.csv to include only complete years from 2022 to 2024.\n",
      "Trimmed data - 2024-05-20T124730.864.csv to include only complete years from 2014 to 2017.\n",
      "Trimmed data - 2024-05-20T124752.075.csv to include only complete years from 2015 to 2024.\n",
      "Skipping data - 2024-05-20T124807.334.csv: not enough data for a complete year.\n",
      "Skipping data - 2024-05-20T124822.527.csv: not enough data for a complete year.\n",
      "Skipping data - 2024-05-20T124837.608.csv: not enough data for a complete year.\n",
      "Skipping data - 2024-05-20T124850.575.csv: not enough data for a complete year.\n",
      "Skipping data - 2024-05-20T124904.119.csv: not enough data for a complete year.\n",
      "Trimmed data - 2024-05-20T124922.708.csv to include only complete years from 2018 to 2024.\n",
      "Trimmed data - 2024-05-20T124938.812.csv to include only complete years from 2018 to 2024.\n",
      "Trimmed data - 2024-05-20T125816.729.csv to include only complete years from 2020 to 2024.\n",
      "Trimmed data - 2024-05-20T125835.051.csv to include only complete years from 2021 to 2024.\n",
      "Skipping data - 2024-05-20T125851.756.csv: not enough data for a complete year.\n",
      "Trimmed data - 2024-05-20T125908.232.csv to include only complete years from 2022 to 2024.\n",
      "Trimmed data - 2024-05-20T125929.710.csv to include only complete years from 2018 to 2024.\n",
      "Trimmed data - 2024-05-20T125948.375.csv to include only complete years from 2019 to 2023.\n",
      "Trimmed data - 2024-05-20T130007.153.csv to include only complete years from 2022 to 2024.\n",
      "Trimmed data - 2024-05-20T130024.498.csv to include only complete years from 2022 to 2024.\n",
      "Trimmed data - 2024-05-20T130046.942.csv to include only complete years from 2019 to 2024.\n",
      "Skipping data - 2024-05-20T130059.949.csv: not enough data for a complete year.\n",
      "Trimmed data - 2024-05-20T130120.744.csv to include only complete years from 2020 to 2024.\n",
      "Trimmed data - 2024-05-20T130138.077.csv to include only complete years from 2020 to 2022.\n",
      "Trimmed data - 2024-05-20T130154.738.csv to include only complete years from 2020 to 2022.\n",
      "Trimmed data - 2024-05-20T130216.081.csv to include only complete years from 2021 to 2024.\n",
      "Trimmed data - 2024-05-20T130236.977.csv to include only complete years from 2021 to 2024.\n",
      "Skipping data - 2024-05-20T130250.090.csv: not enough data for a complete year.\n",
      "Skipping data - 2024-05-20T130303.977.csv: not enough data for a complete year.\n",
      "Skipping data - 2024-05-20T130317.884.csv: not enough data for a complete year.\n",
      "Trimmed data - 2024-05-20T130334.152.csv to include only complete years from 2022 to 2024.\n",
      "Trimmed data - 2024-05-20T130354.199.csv to include only complete years from 2021 to 2024.\n",
      "Trimmed data - 2024-05-20T130413.161.csv to include only complete years from 2021 to 2024.\n",
      "Trimmed data - 2024-05-20T130430.770.csv to include only complete years from 2021 to 2024.\n",
      "Trimmed data - 2024-05-20T130450.153.csv to include only complete years from 2021 to 2024.\n",
      "Trimmed data - 2024-05-20T130508.731.csv to include only complete years from 2020 to 2023.\n",
      "Trimmed data - 2024-05-20T130526.452.csv to include only complete years from 2021 to 2024.\n",
      "Skipping data - 2024-05-20T130540.416.csv: not enough data for a complete year.\n",
      "Trimmed data - 2024-05-20T130556.692.csv to include only complete years from 2022 to 2024.\n",
      "Skipping data - 2024-05-20T130611.445.csv: not enough data for a complete year.\n",
      "Trimmed data - 2024-05-20T130626.959.csv to include only complete years from 2022 to 2024.\n",
      "Trimmed data - 2024-05-20T130644.090.csv to include only complete years from 2016 to 2022.\n",
      "Skipping data - 2024-05-20T130658.544.csv: not enough data for a complete year.\n",
      "Trimmed data - 2024-05-20T130716.718.csv to include only complete years from 2021 to 2024.\n",
      "Skipping data - 2024-05-20T130730.227.csv: not enough data for a complete year.\n",
      "Trimmed data - 2024-05-20T130746.672.csv to include only complete years from 2022 to 2024.\n",
      "Trimmed data - 2024-05-20T130804.626.csv to include only complete years from 2022 to 2024.\n",
      "Skipping data - 2024-05-20T130818.968.csv: not enough data for a complete year.\n",
      "Skipping data - 2024-05-20T130833.927.csv: not enough data for a complete year.\n",
      "Trimmed data 2(33).csv to include only complete years from 2018 to 2022.\n",
      "Trimmed data 2(34).csv to include only complete years from 2018 to 2022.\n",
      "Trimmed data 2(35).csv to include only complete years from 2018 to 2021.\n",
      "Trimmed data 2(36).csv to include only complete years from 2020 to 2024.\n",
      "Trimmed data 2(37).csv to include only complete years from 2020 to 2024.\n",
      "Trimmed data 2(38).csv to include only complete years from 2020 to 2024.\n",
      "Trimmed data 2(39).csv to include only complete years from 2017 to 2024.\n",
      "Trimmed data 2(40).csv to include only complete years from 2017 to 2024.\n",
      "Trimmed data 2(41).csv to include only complete years from 2020 to 2024.\n",
      "Skipping data 2(42).csv: not enough data for a complete year.\n",
      "Skipping data 2(43).csv: not enough data for a complete year.\n",
      "Skipping data 2(44).csv: not enough data for a complete year.\n",
      "Skipping data 2(45).csv: not enough data for a complete year.\n",
      "Trimmed data 2(46).csv to include only complete years from 2018 to 2022.\n",
      "Trimmed data 2(47).csv to include only complete years from 2019 to 2024.\n",
      "Trimmed data 2(48).csv to include only complete years from 2020 to 2023.\n",
      "Skipping data 2(49).csv: not enough data for a complete year.\n",
      "Trimmed data 2(50).csv to include only complete years from 2018 to 2020.\n",
      "Trimmed data 2(51).csv to include only complete years from 2021 to 2024.\n",
      "Trimmed data 2(52).csv to include only complete years from 2019 to 2023.\n",
      "Trimmed data 2(53).csv to include only complete years from 2022 to 2024.\n",
      "Trimmed data 2(54).csv to include only complete years from 2020 to 2022.\n",
      "Trimmed data 2(55).csv to include only complete years from 2020 to 2024.\n",
      "Trimmed data 2(56).csv to include only complete years from 2021 to 2024.\n",
      "Skipping data 2(57).csv: not enough data for a complete year.\n",
      "Skipping data 2(58).csv: not enough data for a complete year.\n",
      "Trimmed data 2(59).csv to include only complete years from 2019 to 2022.\n",
      "Trimmed data 2(60).csv to include only complete years from 2019 to 2023.\n",
      "Skipping data 2(61).csv: not enough data for a complete year.\n",
      "Skipping data 2(62).csv: not enough data for a complete year.\n",
      "Trimmed data 2(63).csv to include only complete years from 2021 to 2024.\n",
      "Trimmed data 2(64).csv to include only complete years from 2020 to 2024.\n",
      "Trimmed data 2(65).csv to include only complete years from 2021 to 2024.\n",
      "Skipping data 2(66).csv: not enough data for a complete year.\n",
      "Skipping data 2(67).csv: not enough data for a complete year.\n",
      "Trimmed data 2(68).csv to include only complete years from 2021 to 2024.\n",
      "Trimmed data 2(69).csv to include only complete years from 2020 to 2024.\n",
      "Trimmed data 2(70).csv to include only complete years from 2021 to 2024.\n",
      "Trimmed data 2(71).csv to include only complete years from 2018 to 2024.\n",
      "Trimmed data 2(72).csv to include only complete years from 2022 to 2024.\n",
      "Trimmed data 2(73).csv to include only complete years from 2021 to 2024.\n",
      "Trimmed data 2(74).csv to include only complete years from 2021 to 2024.\n",
      "Trimmed data 2(75).csv to include only complete years from 2018 to 2024.\n",
      "Trimmed data 2(76).csv to include only complete years from 2022 to 2024.\n",
      "Trimmed data 2(77).csv to include only complete years from 2022 to 2024.\n",
      "Trimmed data 2(78).csv to include only complete years from 2021 to 2024.\n",
      "Trimmed data 2(79).csv to include only complete years from 2019 to 2024.\n",
      "Trimmed data 2(80).csv to include only complete years from 2017 to 2021.\n",
      "Trimmed data 2(81).csv to include only complete years from 2018 to 2020.\n",
      "Trimmed data 2(82).csv to include only complete years from 2018 to 2022.\n",
      "Trimmed data 2(83).csv to include only complete years from 2018 to 2022.\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import os\n",
    "from datetime import datetime\n",
    "input_dir = f\"{root_dir}/Data/Downloads/Raw\"\n",
    "output_dir = f\"{root_dir}/Data/Downloads/Trimmed\"\n",
    "num_years = 3 # Set the minimum number of years needed for site to be included\n",
    "def trim_to_complete_years(input_dir, output_dir):\n",
    "    if not os.path.exists(output_dir):\n",
    "        os.makedirs(output_dir)\n",
    "\n",
    "    for filename in os.listdir(input_dir):\n",
    "        if filename.endswith('.csv'):\n",
    "            input_path = os.path.join(input_dir, filename)\n",
    "            df = pd.read_csv(input_path, skiprows=[1])  # Skip the units row\n",
    "            \n",
    "            # Parse dates with the given format\n",
    "            df['Date'] = pd.to_datetime(df['Date'], format='%Y-%m-%d')\n",
    "            \n",
    "            start_year = df['Date'].dt.year.min()\n",
    "            end_year = df['Date'].dt.year.max()\n",
    "            \n",
    "            # Calculate the number of unique years in the data\n",
    "            unique_years = df['Date'].dt.year.nunique()\n",
    "            \n",
    "            if unique_years < num_years:\n",
    "                print(f'Skipping {filename}: not enough data for a complete year.')\n",
    "                continue\n",
    "            \n",
    "            # Find the first and last complete years\n",
    "            start_date = datetime(start_year, 1, 1)\n",
    "            end_date = datetime(end_year, 12, 31)\n",
    "            \n",
    "            # Trim the dataframe to complete years\n",
    "            df_trimmed = df[(df['Date'] >= start_date) & (df['Date'] <= end_date)]\n",
    "            \n",
    "            # Save the trimmed dataframe to the new directory\n",
    "            output_path = os.path.join(output_dir, filename)\n",
    "            df_trimmed.to_csv(output_path, index=False)\n",
    "            \n",
    "            print(f'Trimmed {filename} to include only complete years from {start_year} to {end_year}.')\n",
    "\n",
    "# Example usage\n",
    "\n",
    "\n",
    "# Example usage\n",
    "\n",
    "\n",
    "trim_to_complete_years(input_dir, output_dir)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95bfc08b",
   "metadata": {},
   "source": [
    "#### Find the energy losses at the simulated AC:DC ratios"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "752e863e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>521.337399</td>\n",
       "      <td>457.303212</td>\n",
       "      <td>396.236662</td>\n",
       "      <td>338.375741</td>\n",
       "      <td>283.791955</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>169</th>\n",
       "      <td>93.881489</td>\n",
       "      <td>86.367258</td>\n",
       "      <td>79.164440</td>\n",
       "      <td>72.285706</td>\n",
       "      <td>65.740067</td>\n",
       "      <td>59.499528</td>\n",
       "      <td>53.541890</td>\n",
       "      <td>47.910613</td>\n",
       "      <td>42.595090</td>\n",
       "      <td>37.636743</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>170</th>\n",
       "      <td>407.424782</td>\n",
       "      <td>366.000097</td>\n",
       "      <td>326.721479</td>\n",
       "      <td>289.252308</td>\n",
       "      <td>253.527213</td>\n",
       "      <td>219.456012</td>\n",
       "      <td>187.218960</td>\n",
       "      <td>156.723030</td>\n",
       "      <td>128.091253</td>\n",
       "      <td>101.357107</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171</th>\n",
       "      <td>682.132699</td>\n",
       "      <td>614.469430</td>\n",
       "      <td>549.291330</td>\n",
       "      <td>486.814706</td>\n",
       "      <td>427.181371</td>\n",
       "      <td>370.267635</td>\n",
       "      <td>316.364620</td>\n",
       "      <td>265.942378</td>\n",
       "      <td>218.468819</td>\n",
       "      <td>174.223615</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172</th>\n",
       "      <td>601.055117</td>\n",
       "      <td>542.034176</td>\n",
       "      <td>485.600766</td>\n",
       "      <td>431.811397</td>\n",
       "      <td>380.675544</td>\n",
       "      <td>332.249807</td>\n",
       "      <td>286.585878</td>\n",
       "      <td>243.508388</td>\n",
       "      <td>203.007625</td>\n",
       "      <td>164.917300</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>173 rows × 52 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          1:2.00       1:1.98       1:1.96       1:1.94       1:1.92  \\\n",
       "0     796.386796   722.589871   651.557329   583.157283   517.378273   \n",
       "1    1167.788606  1089.509567  1013.749848   940.693651   870.440511   \n",
       "2     704.560486   624.351660   547.149767   473.336985   402.792695   \n",
       "3     929.454035   844.680537   762.751112   684.121087   608.867942   \n",
       "4    1588.339591  1481.007394  1376.607828  1275.347337  1177.375134   \n",
       "..           ...          ...          ...          ...          ...   \n",
       "168   886.203572   807.682618   731.706425   658.660081   588.442195   \n",
       "169    93.881489    86.367258    79.164440    72.285706    65.740067   \n",
       "170   407.424782   366.000097   326.721479   289.252308   253.527213   \n",
       "171   682.132699   614.469430   549.291330   486.814706   427.181371   \n",
       "172   601.055117   542.034176   485.600766   431.811397   380.675544   \n",
       "\n",
       "          1:1.90      1:1.88      1:1.86      1:1.84      1:1.82  ...  1:1.18  \\\n",
       "0     454.497040  394.740434  337.927062  284.189776  233.846119  ...     NaN   \n",
       "1     802.808155  737.746629  674.978533  614.769557  556.703902  ...     NaN   \n",
       "2     335.876310  272.822892  214.005977  159.478896  109.753307  ...     NaN   \n",
       "3     537.048685  468.565219  403.264689  340.999977  281.863592  ...     NaN   \n",
       "4    1082.468792  990.394058  901.480124  816.000005  733.421510  ...     NaN   \n",
       "..           ...         ...         ...         ...         ...  ...     ...   \n",
       "168   521.337399  457.303212  396.236662  338.375741  283.791955  ...     NaN   \n",
       "169    59.499528   53.541890   47.910613   42.595090   37.636743  ...     NaN   \n",
       "170   219.456012  187.218960  156.723030  128.091253  101.357107  ...     NaN   \n",
       "171   370.267635  316.364620  265.942378  218.468819  174.223615  ...     NaN   \n",
       "172   332.249807  286.585878  243.508388  203.007625  164.917300  ...     NaN   \n",
       "\n",
       "     1:1.16  1:1.14  1:1.12  1:1.10  1:1.08  1:1.06  1:1.04  1:1.02  1:1.00  \n",
       "0       NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN  \n",
       "1       NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN  \n",
       "2       NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN  \n",
       "3       NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN  \n",
       "4       NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN  \n",
       "..      ...     ...     ...     ...     ...     ...     ...     ...     ...  \n",
       "168     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN  \n",
       "169     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN  \n",
       "170     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN  \n",
       "171     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN  \n",
       "172     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN  \n",
       "\n",
       "[173 rows x 52 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing complete. Results saved to 'processed_statistics1.csv', 'summary_statistics1.csv', and 'transposed_summary_statistics1.csv'.\n",
      "Overall Total Generated (MWh): 1023816.0896816883\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import os\n",
    "directory = f'{root_dir}/Data/Downloads/Trimmed'\n",
    "# Function to process a single CSV\n",
    "def process_csv(filepath):\n",
    "    try:\n",
    "        # Read the CSV into a DataFrame\n",
    "        data = pd.read_csv(filepath)\n",
    "        step = 1\n",
    "        # Ensure columns are sorted and units rows are excluded\n",
    "        data = data.iloc[2:].copy()\n",
    "        \n",
    "        # Convert 'Capacity Factor' and 'Electricity Generated' columns to numeric, forcing errors to NaN\n",
    "        data['Capacity Factor'] = pd.to_numeric(data['Capacity Factor'], errors='coerce')\n",
    "        data['Electricity Generated'] = pd.to_numeric(data['Electricity Generated'], errors='coerce')\n",
    "        \n",
    "        # Drop rows with NaN values in these columns\n",
    "        data.dropna(subset=['Capacity Factor', 'Electricity Generated'], inplace=True)\n",
    "        \n",
    "        # Sort the DataFrame by 'Capacity Factor' from largest to smallest\n",
    "        data_sorted = data.sort_values(by='Capacity Factor', ascending=False)\n",
    "        \n",
    "        # Calculate total generated electricity in KWh\n",
    "        total_gen_kwh = (data_sorted['Electricity Generated'].sum()/data_sorted['Electricity Generated'].count())*8760\n",
    "        \n",
    "        # Calculate the energy produced exceeding a given capacity factor\n",
    "        x_counts = {}\n",
    "        for x in range(50, 100+step, step):\n",
    "            # Apply the condition to get values where (data_sorted['Capacity Factor'] - x) > 0\n",
    "            valid_rows = data_sorted[data_sorted['Capacity Factor'] > x]\n",
    "            if not valid_rows.empty:\n",
    "                x_counts[f'1:{1+((200 - (x*2))/100):.2f}'] = (\n",
    "                    (valid_rows['Electricity Generated'] * ((valid_rows['Capacity Factor'] - x) / 100)).sum() / 1000\n",
    "                )  # Convert KWh to MWh\n",
    "        \n",
    "        # Return the results as a dictionary\n",
    "        results = x_counts\n",
    "        results['Total Gen (KWh)'] = total_gen_kwh\n",
    "        \n",
    "        return results\n",
    "    except Exception as e:\n",
    "        print(f\"Error processing file {filepath}: {e}\")\n",
    "        return None\n",
    "\n",
    "# Directory containing the CSV files\n",
    "\n",
    "\n",
    "# Initialize a list to hold the results\n",
    "results_list = []\n",
    "total_gen_kwh_sum = 0\n",
    "\n",
    "# Process each CSV in the directory\n",
    "for filename in os.listdir(directory):\n",
    "    if filename.endswith('.csv'):\n",
    "        filepath = os.path.join(directory, filename)\n",
    "        result = process_csv(filepath)\n",
    "        if result is not None:\n",
    "            results_list.append(result)\n",
    "            total_gen_kwh_sum += result['Total Gen (KWh)']\n",
    "\n",
    "# Convert total generation sum from KWh to MWh\n",
    "total_gen_mwh_sum = (total_gen_kwh_sum / 1000)\n",
    "\n",
    "# Create a new DataFrame with the results\n",
    "results_df = pd.DataFrame(results_list)\n",
    "display(results_df)\n",
    "# Sum all columns to create one value per x value\n",
    "column_sums = results_df.sum()\n",
    "\n",
    "# Divide all clipping values by the overall total generated value\n",
    "for col in column_sums.index:\n",
    "    if 'Clipping at' in col:\n",
    "        column_sums[col] = (column_sums[col]/total_gen_mwh_sum)*100\n",
    "\n",
    "# Create the summary DataFrame\n",
    "summary = pd.DataFrame({\n",
    "    'Metric': column_sums.index,\n",
    "    'Value': column_sums.values\n",
    "})\n",
    "\n",
    "# Add the overall total generation to the summary DataFrame\n",
    "summary = summary._append({'Metric': 'Overall Total Generated (MWh)', 'Value': total_gen_mwh_sum}, ignore_index=True)\n",
    "\n",
    "# Transpose the summary DataFrame so each clipping value is its own row\n",
    "transposed_summary = summary.set_index('Metric').transpose().reset_index(drop=True)\n",
    "\n",
    "\n",
    "print(\"Processing complete. Results saved to 'processed_statistics1.csv', 'summary_statistics1.csv', and 'transposed_summary_statistics1.csv'.\")\n",
    "print(f\"Overall Total Generated (MWh): {total_gen_mwh_sum}\")\n",
    "\n",
    "# Optional: Print the transposed summary DataFrame\n",
    "\n",
    "# Find the value of \"Overall Total Generated (MWh)\"\n",
    "overall_total = summary.loc[summary['Metric'] == 'Overall Total Generated (MWh)', 'Value'].values[0]\n",
    "\n",
    "# Divide all values by the overall total\n",
    "summary['Value'] = (summary['Value'] / overall_total)*100\n",
    "\n",
    "#display(summary)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "afba8f6e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "\n",
    "plot_name = 'AC_DC_Ratios'\n",
    "# Filter the DataFrame to only include rows with 'AC:DC' in the 'Metric' column\n",
    "filtered_df = summary[summary['Metric'].str.contains('1')]\n",
    "\n",
    "# Sort the DataFrame by 'Value' in ascending order\n",
    "sorted_df = filtered_df.sort_values(by='Value')\n",
    "results_df = sorted_df\n",
    "results_df['Ratio'] = results_df['Metric'].str.split(':').str[1]\n",
    "#display(results_df)\n",
    "sorted_df.to_csv(f'{tables}/Outputs/DCAC_Ratios.csv')\n",
    "# Plotting the data\n",
    "plt.figure(figsize=(9, 5))\n",
    "plt.bar(sorted_df['Metric'], sorted_df['Value'], color='blue')\n",
    "plt.xlabel('AC:DC Ratio', fontdict=font_properties)\n",
    "plt.ylabel('Percent of Yearly Energy Lost', fontdict=font_properties)\n",
    "#plt.title('Bar Graph of Filtered AC:DC Metrics')\n",
    "\n",
    "plt.xticks(rotation=45, ha='right')\n",
    "plt.tight_layout()\n",
    "plt.savefig(f'{figures}/{plot_name}.jpg', dpi=300, format='jpg')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44de7461",
   "metadata": {},
   "source": [
    "### Future Site Scenario Shapes Summary\n",
    "this script calculates total area covered, average site size, mean distance from transmission lines, area to perimeter ratio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "485afe2e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Scenario</th>\n",
       "      <th>Site Type</th>\n",
       "      <th>Average Polygon Size (ha)</th>\n",
       "      <th>Number of Sites</th>\n",
       "      <th>Mean Distance from Transmission Lines (m)</th>\n",
       "      <th>Area to Perimeter Ratio (m²/m)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>No Land Use Restrictions</td>\n",
       "      <td>UT</td>\n",
       "      <td>96</td>\n",
       "      <td>1050</td>\n",
       "      <td>286</td>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>No Land Use Restrictions</td>\n",
       "      <td>SAT</td>\n",
       "      <td>95</td>\n",
       "      <td>842</td>\n",
       "      <td>274</td>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>No Land Use Restrictions</td>\n",
       "      <td>Large</td>\n",
       "      <td>369</td>\n",
       "      <td>238</td>\n",
       "      <td>216</td>\n",
       "      <td>179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>No Agricultural Land</td>\n",
       "      <td>UT</td>\n",
       "      <td>7</td>\n",
       "      <td>14812</td>\n",
       "      <td>730</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>No Agricultural Land</td>\n",
       "      <td>SAT</td>\n",
       "      <td>7</td>\n",
       "      <td>12083</td>\n",
       "      <td>732</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>No Agricultural Land</td>\n",
       "      <td>Large</td>\n",
       "      <td>207</td>\n",
       "      <td>426</td>\n",
       "      <td>242</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>No MSG 1-4</td>\n",
       "      <td>UT</td>\n",
       "      <td>19</td>\n",
       "      <td>5188</td>\n",
       "      <td>585</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>No MSG 1-4</td>\n",
       "      <td>SAT</td>\n",
       "      <td>20</td>\n",
       "      <td>4102</td>\n",
       "      <td>567</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>No MSG 1-4</td>\n",
       "      <td>Large</td>\n",
       "      <td>204</td>\n",
       "      <td>432</td>\n",
       "      <td>286</td>\n",
       "      <td>86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>No Row Crops of MSG 1-4</td>\n",
       "      <td>UT</td>\n",
       "      <td>12</td>\n",
       "      <td>8343</td>\n",
       "      <td>664</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>No Row Crops of MSG 1-4</td>\n",
       "      <td>SAT</td>\n",
       "      <td>12</td>\n",
       "      <td>6767</td>\n",
       "      <td>655</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>No Row Crops of MSG 1-4</td>\n",
       "      <td>Large</td>\n",
       "      <td>191</td>\n",
       "      <td>462</td>\n",
       "      <td>264</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>None</td>\n",
       "      <td>Detected</td>\n",
       "      <td>7</td>\n",
       "      <td>517</td>\n",
       "      <td>1685</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Scenario Site Type  Average Polygon Size (ha)  \\\n",
       "0   No Land Use Restrictions        UT                         96   \n",
       "1   No Land Use Restrictions       SAT                         95   \n",
       "2   No Land Use Restrictions     Large                        369   \n",
       "3       No Agricultural Land        UT                          7   \n",
       "4       No Agricultural Land       SAT                          7   \n",
       "5       No Agricultural Land     Large                        207   \n",
       "6                 No MSG 1-4        UT                         19   \n",
       "7                 No MSG 1-4       SAT                         20   \n",
       "8                 No MSG 1-4     Large                        204   \n",
       "9    No Row Crops of MSG 1-4        UT                         12   \n",
       "10   No Row Crops of MSG 1-4       SAT                         12   \n",
       "11   No Row Crops of MSG 1-4     Large                        191   \n",
       "12                      None  Detected                          7   \n",
       "\n",
       "    Number of Sites  Mean Distance from Transmission Lines (m)  \\\n",
       "0              1050                                        286   \n",
       "1               842                                        274   \n",
       "2               238                                        216   \n",
       "3             14812                                        730   \n",
       "4             12083                                        732   \n",
       "5               426                                        242   \n",
       "6              5188                                        585   \n",
       "7              4102                                        567   \n",
       "8               432                                        286   \n",
       "9              8343                                        664   \n",
       "10             6767                                        655   \n",
       "11              462                                        264   \n",
       "12              517                                       1685   \n",
       "\n",
       "    Area to Perimeter Ratio (m²/m)  \n",
       "0                               89  \n",
       "1                               89  \n",
       "2                              179  \n",
       "3                               25  \n",
       "4                               25  \n",
       "5                               56  \n",
       "6                               38  \n",
       "7                               38  \n",
       "8                               86  \n",
       "9                               31  \n",
       "10                              31  \n",
       "11                              69  \n",
       "12                              42  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "\n",
    "dist_overide = 0  # set this variable to -1 to force the script to recalculate the distance between polygons and transmission lines\n",
    "det_layer = 'Detected_Sites'\n",
    "feature_class = 'Potential_Future_Sites'\n",
    "gdf = gpd.read_file(f'{gdb_path}', layer=feature_class)\n",
    "dgdf = gpd.read_file(f'{gdb_path}', layer=det_layer)\n",
    "lines = 'transmission_lines'\n",
    "transmission_lines = gpd.read_file(f'{gdb_path}', layer=lines)\n",
    "gdf = gdf.to_crs(epsg=6347)\n",
    "transmission_lines = transmission_lines.to_crs(epsg=6347)\n",
    "\n",
    "scen_dict = {'No_Restrictions': 'No Land Use Restrictions', 'NoAg': \"No Agricultural Land\", 'NoMSG1_4': \"No MSG 1-4\", 'NoRowCrops_or_MSG14': 'No Row Crops of MSG 1-4'}\n",
    "#display(gdf.iloc[1:2])\n",
    "# Set \"Scenario\" field for dgdf to \"Detected\"\n",
    "dgdf['Scenario'] = 'Detected'\n",
    "dgdf['Scenario and Type'] = 'Detected'\n",
    "dgdf['Type'] = 'Detected'\n",
    "\n",
    "# Calculate the nearest distance from each polygon to the line\n",
    "def calculate_nearest_distance(polygon, lines):\n",
    "    nearest_point = nearest_points(polygon, lines)[1]\n",
    "    return polygon.distance(nearest_point)\n",
    "\n",
    "# Apply the distance calculation to each polygon if the field does not exist or if overide is set to -1\n",
    "if 'NEAR_DIST' not in gdf.columns or dist_overide == -1:\n",
    "    union_lines = transmission_lines.geometry.union_all()\n",
    "    #print(f\"No Nearest distance field (NEAR_DIST) detected, calculating distance between {feature_class} and {lines} now.\")\n",
    "    gdf['NEAR_DIST'] = gdf.geometry.apply(lambda geom: calculate_nearest_distance(geom, union_lines))\n",
    "    gdf.to_file(gdb_path, layer=feature_class)\n",
    "    #print(\"Nearest distances calculated\")\n",
    "\n",
    "if 'NEAR_DIST' not in dgdf.columns or dist_overide == -1:\n",
    "    #print(f\"No Nearest distance field (NEAR_DIST) detected, calculating distance between {det_layer} and {lines} now.\")\n",
    "    union_lines = transmission_lines.geometry.union_all()\n",
    "    dgdf['NEAR_DIST'] = dgdf.geometry.apply(lambda geom: calculate_nearest_distance(geom, union_lines))\n",
    "    dgdf.to_file(gdb_path, layer=det_layer)\n",
    "   # print(\"Nearest distances calculated\")\n",
    "\n",
    "# Merge dgdf with gdf\n",
    "merged_gdf = pd.concat([gdf, dgdf], ignore_index=True)\n",
    "\n",
    "site_scenarios = merged_gdf['Scenario and Type'].unique()\n",
    "#print(site_scenarios)\n",
    "# Iterate through each scenario folder and process the shapefiles\n",
    "results_lis = []\n",
    "cols = ['Scenario', 'Site Type', 'Average Polygon Size (ha)', 'Number of Sites', 'Mean Distance from Transmission Lines (m)', f'Area to Perimeter Ratio (m\\N{SUPERSCRIPT TWO}/m)']\n",
    "\n",
    "for scenario in site_scenarios:\n",
    "    sublis = []\n",
    "    # Load the shapefile\n",
    "    gdf_lis = merged_gdf[merged_gdf['Scenario and Type'] == scenario]\n",
    "\n",
    "    # Calculate total area\n",
    "    total_area = (gdf_lis.geometry.area.sum()) / 10000\n",
    "    total_area = int(round(total_area, 0))\n",
    "    #print(f'TotalArea: {total_area}')\n",
    "    mean_area = (gdf_lis.geometry.area.mean()) / 10000\n",
    "    mean_area = int(round(mean_area, 0))\n",
    "    sites_n = gdf_lis['Scenario'].count()\n",
    "    site_type = gdf_lis['Type'].unique()\n",
    "    site_type = str(site_type[0])\n",
    "    scenario = gdf_lis['Scenario'].unique()\n",
    "    #print(f'Scenario/Site: {scenario}')\n",
    "    scenario = str(scenario[0])\n",
    "\n",
    "    scenario = scen_dict.get(scenario)\n",
    "\n",
    "    area_to_perimeter_ratio = int(round(((gdf_lis.geometry.area / gdf_lis.geometry.length).mean()), 0))\n",
    "    mean_distance = gdf_lis['NEAR_DIST'].mean()\n",
    "    mean_distance = int(round((mean_distance), 0))\n",
    "    all = [scenario, site_type, mean_area, sites_n, mean_distance, area_to_perimeter_ratio]\n",
    "    sublis.extend(all)\n",
    "    results_lis.append(sublis)\n",
    "\n",
    "results = pd.DataFrame(results_lis, columns=cols)\n",
    "display(results)\n",
    "\n",
    "excel_path = fr'{tables}\\Outputs\\Future_Site_Qualities.xlsx'\n",
    "\n",
    "# Save DataFrame to Excel\n",
    "results.to_excel(excel_path, index=False)\n",
    "# Open the Excel file and format it\n",
    "wb = load_workbook(excel_path)\n",
    "ws = wb.active\n",
    "\n",
    "# Apply formatting to all cells\n",
    "font = Font(name='Times New Roman', size=12)\n",
    "bold_font = Font(name='Times New Roman', size=12, bold=True)\n",
    "\n",
    "# Apply font to all cells\n",
    "for row in ws.iter_rows():\n",
    "    for cell in row:\n",
    "        cell.font = font\n",
    "\n",
    "# Apply bold font to the first row (header)\n",
    "for cell in ws[1]:\n",
    "    cell.font = bold_font\n",
    "\n",
    "# Adjust column widths\n",
    "for col in ws.columns:\n",
    "    max_length = 0\n",
    "    column = col[0].column_letter  # Get the column name\n",
    "    for cell in col:\n",
    "        try:\n",
    "            if len(str(cell.value)) > max_length:\n",
    "                max_length = len(cell.value)\n",
    "        except:\n",
    "            pass\n",
    "    adjusted_width = (max_length + 2)\n",
    "    ws.column_dimensions[column].width = adjusted_width\n",
    "\n",
    "# Save the formatted Excel file\n",
    "wb.save(excel_path)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a1ca06bd",
   "metadata": {},
   "source": [
    "### Calculate DC Capacity Needed to Meet Goals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "eaaf2d74",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0017713939829193102\n",
      "SAT future capacity (MW): 89356.61 \n",
      "UT Future Capacity (MW): 95666.46\n"
     ]
    }
   ],
   "source": [
    "future_energy_capacity = 95525 # GWh Based on scenario 4 of the CLCPA Scoping plan\n",
    "AC_DC = 1.5\n",
    "inverter_loss = .05\n",
    "# Function to calculate future capacity based on the capacity factor\n",
    "def calculate_future_capacity(future_energy_capacity, capacity_factor, clip_loss, inverter_loss):\n",
    "    raw_cap= (future_energy_capacity * 1000) / ((capacity_factor/100)* 8766)\n",
    "    raw_cap *= (1+clip_loss)\n",
    "    raw_cap *= (1+inverter_loss)\n",
    "    \n",
    "    return raw_cap\n",
    "\n",
    "\n",
    "ratio_df = pd.read_csv(fr'{tables}\\Outputs\\DCAC_Ratios.csv') \n",
    "ratio_df.set_index(\"Ratio\", inplace=True)\n",
    "clip_loss = ratio_df.loc[AC_DC, 'Value']\n",
    "clip_loss /= 100\n",
    "print(clip_loss)\n",
    "\n",
    "\n",
    "# Calculate future capacities for SAT and UT\n",
    "SAT_future_capacity_DC = calculate_future_capacity(future_energy_capacity, sat_mean, clip_loss, inverter_loss)\n",
    "UT_future_capacity_DC = calculate_future_capacity(future_energy_capacity, fa_mean, clip_loss, inverter_loss)\n",
    "print(f'SAT future capacity (MW): {round(SAT_future_capacity_DC,2)} \\nUT Future Capacity (MW): {round(UT_future_capacity_DC, 2)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5ac8d9d",
   "metadata": {},
   "source": [
    "### Current and Future Capacities\n",
    "Calculates the rate of capacity growth for SAT and FA sites based off of existing trends and expected future capacity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "id": "6ab8e1ed",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initial fit for a=14.79762141476133, b=2.2751710459226833\n",
      "Optimized parameters for future capacity 89356.61090488367: a = 5.2445, b = 2.7404\n",
      "Power function form: y = 5.2445 * x^2.7404\n",
      "Initial fit for a=14.79762141476133, b=2.2751710459226833\n",
      "Optimized parameters for future capacity 95666.46109519759: a = 4.6584, b = 2.7930\n",
      "Power function form: y = 4.6584 * x^2.7930\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "end_yr = 2050\n",
    "num_cols = 1\n",
    "\n",
    "x_dim, y_dim = 6, 4  # Adjust dimensions accordingly\n",
    "plot_name = \"capacity trends\"\n",
    "\n",
    "# Function to perform curve fitting and optimization\n",
    "def optimize_capacity(data, years, actual, future_year, future_capacity, initial_guess=[0.1, 1], future_weight=1000):\n",
    "    def power_function(x, a, b):\n",
    "        return a * np.power(x, b)\n",
    "\n",
    "    def objective(params, x_data, y_data, future_year, future_capacity, future_weight):\n",
    "        a, b = params\n",
    "        predicted = power_function(x_data, a, b)\n",
    "        future_predicted = power_function(future_year, a, b)\n",
    "        error = np.sum((y_data - predicted)**2)\n",
    "        future_error = (future_capacity - future_predicted)**2\n",
    "        return error + future_weight * future_error\n",
    "\n",
    "    # Perform the curve fitting\n",
    "    params, covariance = curve_fit(power_function, years, actual, p0=initial_guess, maxfev=100000)\n",
    "    a, b = params\n",
    "\n",
    "    print(f\"Initial fit for a={a}, b={b}\")\n",
    "\n",
    "    # Perform the optimization\n",
    "    bounds = [(0, np.inf), (0, np.inf)]\n",
    "    result = minimize(objective, params, args=(years, actual, future_year, future_capacity, future_weight), bounds=bounds)\n",
    "    \n",
    "    # Print the optimized parameters in power function form\n",
    "    print(f\"Optimized parameters for future capacity {future_capacity}: a = {result.x[0]:.4f}, b = {result.x[1]:.4f}\")\n",
    "    print(f\"Power function form: y = {result.x[0]:.4f} * x^{result.x[1]:.4f}\")\n",
    "    \n",
    "    return result.x, power_function\n",
    "\n",
    "# Function to plot the data with an option for subplots\n",
    "def plot_results(data, power_function, x_fit_opt, y_fit_opt, future_year, future_capacity, \n",
    "                 figure_title, subplot_index, ax, show_legend=True):\n",
    "    \n",
    "    ax.scatter(data['Normalized Year'], data['Total Cumulative Capacity'], label='Data Points')\n",
    "    ax.plot(x_fit_opt, y_fit_opt, color='orange', linestyle='--', label='Optimized Projection')\n",
    "    ax.scatter(future_year, future_capacity, color='green', label='Future Goal')\n",
    "    ax.set_xlabel('Year')\n",
    "    ax.set_ylabel('Statewide Capacity (MW$_{DC}$)')\n",
    "    ax.set_title(figure_title)\n",
    "    \n",
    "    def thousands_formatter(x, pos):\n",
    "        x /= 1000\n",
    "        return f'{x:,.0f}'\n",
    "\n",
    "    ax.yaxis.set_major_formatter(ticker.FuncFormatter(thousands_formatter))\n",
    "    ax.set_xticks([-2, 8, 18, 28, 38])\n",
    "    ax.set_xticklabels(['2010', '2020', '2030', '2040', '2050'])\n",
    "    \n",
    "    if show_legend:\n",
    "        ax.legend()\n",
    "\n",
    "data_path = fr'{tables}/Outputs/Completed_Capacity_Over_Time.csv'\n",
    "data = pd.read_csv(data_path)\n",
    "data['Normalized Year'] = ((data['Year']) - (data['Year'].min())) + 1\n",
    "\n",
    "max_year = int(data['Year'].max())\n",
    "future_year = end_yr - (data['Year'].min())\n",
    "years = data['Normalized Year'][0:11]\n",
    "actual = data['Total Cumulative Capacity'][0:11]\n",
    "\n",
    "# Use the same initial guess for both SAT and UT\n",
    "initial_guess = [0.1, 1]  # Initial guess that worked well for UT\n",
    "\n",
    "# Optimize for SAT and UT with a stronger future constraint\n",
    "future_weight = 1000  # Increase this value to give more importance to hitting the future capacity goal\n",
    "SAT_params, power_function = optimize_capacity(data, years, actual, future_year, SAT_future_capacity_DC, initial_guess=initial_guess, future_weight=future_weight)\n",
    "UT_params, _ = optimize_capacity(data, years, actual, future_year, UT_future_capacity_DC, initial_guess=initial_guess, future_weight=future_weight)\n",
    "\n",
    "# Extend the data for future years\n",
    "min_year = int(data['Year'].min()) \n",
    "normalized_end_year = end_yr - int(data['Year'].min()) \n",
    "new_years = pd.DataFrame({'Normalized Year': range(int(max(data['Normalized Year'])) + 1, normalized_end_year + 1)})\n",
    "data = pd.concat([data, new_years], ignore_index=True)\n",
    "data['Year'] = data['Normalized Year'] + min_year\n",
    "\n",
    "# Generate points for the optimized fitted curves\n",
    "x_fit_opt = np.linspace(min(data['Normalized Year']), future_year, 100)\n",
    "SAT_y_fit_opt = power_function(x_fit_opt, *SAT_params)\n",
    "UT_y_fit_opt = power_function(x_fit_opt, *UT_params)\n",
    "\n",
    "# Create subplots\n",
    "fig, axes = plt.subplots(1, 2, figsize=(x_dim, y_dim))\n",
    "plot_results(data, power_function, x_fit_opt, SAT_y_fit_opt, future_year, SAT_future_capacity_DC, \n",
    "             \"Single Axis Tracked Capacity Growth\", 1, axes[0])\n",
    "plot_results(data, power_function, x_fit_opt, UT_y_fit_opt, future_year, UT_future_capacity_DC, \n",
    "             \"Untracked Capacity Growth\", 2, axes[1], show_legend=False)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig(f'{figures}/{plot_name}.jpg', dpi=600, format='jpg')\n",
    "plt.show()\n",
    "\n",
    "# Save the updated data with predicted capacity\n",
    "data['SAT Projected'] = power_function(data['Normalized Year'], *SAT_params)\n",
    "data['FA Projected'] = power_function(data['Normalized Year'], *UT_params)\n",
    "#display(data)\n",
    "data.to_csv(fr'{tables}/Outputs/Predicted_Capacities.csv', index=False)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5601c044",
   "metadata": {},
   "source": [
    "### Monte-Carlo Analysis\n",
    "This block predicts future area ocupied by solar sites based on power density trends and future capacity estimates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "id": "8e873c18",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\twk54\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\pyogrio\\raw.py:196: UserWarning: Measured (M) geometry types are not supported. Original type 'Measured 3D MultiPolygon' is converted to 'MultiPolygon Z'\n",
      "  return ogr_read(\n",
      "C:\\Users\\twk54\\AppData\\Local\\Temp\\ipykernel_40336\\2649031057.py:31: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n",
      "  slope = model.params[1]\n",
      "C:\\Users\\twk54\\AppData\\Local\\Temp\\ipykernel_40336\\2649031057.py:32: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n",
      "  slope_std_error = model.bse[1]\n",
      "C:\\Users\\twk54\\AppData\\Local\\Temp\\ipykernel_40336\\2649031057.py:31: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n",
      "  slope = model.params[1]\n",
      "C:\\Users\\twk54\\AppData\\Local\\Temp\\ipykernel_40336\\2649031057.py:32: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n",
      "  slope_std_error = model.bse[1]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fixed Axis: Mean = 0.6215015766819094, SE = 0.010335631715655944, Slope = 0.015288898007768772, Slope SE = 0.005774394769199521\n",
      "Single Axis: Mean = 0.588723507374368, SE = 0.011027769578495565, Slope = 0.020445885835678412, Slope SE = 0.009885806471513192\n",
      "10000\n",
      "0.9566673043810981\n",
      "16\n",
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      "10000\n",
      "10000\n"
     ]
    }
   ],
   "source": [
    "\n",
    "feature_class = 'Digitized_Fences'\n",
    "# Load the datasets that will set power density trendsfeature_class = 'Digitized_Fences'\n",
    "gdf = gpd.read_file(f'{gdb_path}', layer=feature_class)\n",
    "gdf = gdf.to_crs(epsg=6347)\n",
    "FA = gdf[gdf['Site_Type'] == 'UT']\n",
    "SAT = gdf[gdf['Site_Type'] == 'SAT']\n",
    "units = \"ha\"\n",
    "#Capacity Data\n",
    "data = pd.read_csv(fr'{tables}/Outputs/Predicted_Capacities.csv')\n",
    "\n",
    "area_per_cell = 10000\n",
    "plot_name = \"Monte Carlo Future Area Analysis\"\n",
    "\n",
    "# Calculate y and x values for Fixed Axis\n",
    "fa_y = FA['Capacity_MW'] / (FA['Shape_Area'] / area_per_cell)\n",
    "fa_x = FA['Application_Year']\n",
    "\n",
    "# Calculate y and x values for Single Axis\n",
    "sat_y = SAT['Capacity_MW'] / (SAT['Shape_Area'] / area_per_cell)\n",
    "sat_x = SAT['Application_Year']\n",
    "\n",
    "# Function to calculate mean, standard error, slope, and slope standard error\n",
    "def calculate_stats(x, y):\n",
    "    # Calculate mean and standard error of y\n",
    "    y_mean = np.mean(y)\n",
    "    y_std_error = np.std(y) / np.sqrt(len(y))\n",
    "    \n",
    "    # Fit linear regression model\n",
    "    x_with_const = sm.add_constant(x)  # Adds a constant term to the predictor\n",
    "    model = sm.OLS(y, x_with_const).fit()\n",
    "    slope = model.params[1]\n",
    "    slope_std_error = model.bse[1]\n",
    "    \n",
    "    return y_mean, y_std_error, slope, slope_std_error\n",
    "\n",
    "# Calculate stats for Fixed Axis\n",
    "fa_y_mean, fa_y_std_error, fa_slope, fa_slope_std_error = calculate_stats(fa_x, fa_y)\n",
    "\n",
    "# Calculate stats for Single Axis\n",
    "sat_y_mean, sa_y_std_error, sat_slope, sat_slope_std_error = calculate_stats(sat_x, sat_y)\n",
    "\n",
    "# Print the results\n",
    "print(f\"Fixed Axis: Mean = {fa_y_mean}, SE = {fa_y_std_error}, Slope = {fa_slope}, Slope SE = {fa_slope_std_error}\")\n",
    "print(f\"Single Axis: Mean = {sat_y_mean}, SE = {sa_y_std_error}, Slope = {sat_slope}, Slope SE = {sat_slope_std_error}\")\n",
    "mean_PD_FA, stdev_PD_UT =fa_y_mean,fa_y_std_error\n",
    "mean_m_FA, stdev_m_UT = fa_slope, (fa_slope_std_error)\n",
    "\n",
    "mean_PD_SAT, stdev_PD_SAT=sat_y_mean,sa_y_std_error\n",
    "mean_m_SAT, stdev_m_SAT= sat_slope, (sat_slope_std_error)\n",
    "\n",
    "\n",
    "\n",
    "data['SAT Yearly Capacity'] = data['SAT Projected'].diff()\n",
    "data['FA Yearly Capacity'] = data['FA Projected'].diff()\n",
    "data = data[(data['Year'] >=2024)]\n",
    "# Mean and standard deviation of the site types\n",
    "# Untracked\n",
    "\n",
    "# Large Sites\n",
    "mean_PD_Lg, stdev_PD_Lg = 0.5025828382547487, 0.045442349000872975# in DC MW/Ha\n",
    "#mean_PD_Lg, stdev_PD_Lg = .1883594, 0.045442349000872975/2.44 #acres\n",
    "mean_m_Lg, stdev_m_Lg = mean_m_SAT, stdev_m_SAT\n",
    "\n",
    "# DC:AC Ratio\n",
    "DCAC_min, DCAC_max = 1.28, 1.48\n",
    "\n",
    "# Number of Simulations\n",
    "n = 10000\n",
    "\n",
    "#Set Variables\n",
    "years = data['Year']\n",
    "\n",
    "print(n)\n",
    "\n",
    "\n",
    "SAT_areas_2040 = []\n",
    "UT_areas_2040 = []\n",
    "Lg_areas_2040 = []\n",
    "SAT_PDs = []\n",
    "SAT_Slopes = []\n",
    "SAT_Slopes_all =[]\n",
    "x=1\n",
    "a=0\n",
    "y=1\n",
    "while x <= n:\n",
    "    a+=1\n",
    "    PD_UT = np.random.normal(mean_PD_FA,stdev_PD_UT)\n",
    "    m_UT = np.random.normal(mean_m_FA, stdev_m_UT)\n",
    "    \n",
    "\n",
    "    PD_SAT = np.random.normal(mean_PD_SAT, stdev_PD_SAT)\n",
    "    SAT_PDs.append(PD_SAT)\n",
    "    m_SAT = np.random.normal(mean_m_SAT, stdev_m_SAT)\n",
    "    SAT_Slopes_all.append(m_SAT)\n",
    "\n",
    "    PD_Lg = np.random.normal(mean_PD_Lg, stdev_PD_Lg)\n",
    "    m_Lg = np.random.normal(mean_m_Lg, stdev_m_Lg)\n",
    "    if m_Lg<=0 or m_UT<=0  or m_SAT<=0:\n",
    "        continue\n",
    "    DCAC = np.random.uniform(DCAC_min, DCAC_max)\n",
    "    SAT_Slopes.append(m_SAT)\n",
    "    #random_numbers = np.random.rand(3)\n",
    "    #normalized_values = random_numbers / random_numbers.sum()\n",
    "\n",
    "    UT_areas = []\n",
    "    SAT_areas = []\n",
    "    Lg_areas = []\n",
    "    # Calculate power density and required area for each year and each site type\n",
    "    for year in years:\n",
    "        # Calculate power density for the year for each site\n",
    "        density_UT = PD_UT + (year - 2022) * m_UT\n",
    "        density_SAT = PD_SAT + (year - 2022) * m_SAT\n",
    "        density_Lg = PD_Lg + (year - 2022) * m_Lg\n",
    "        if density_UT >= 2 or density_SAT >= 2 or density_Lg >= 2:\n",
    "            y+=1\n",
    "            break\n",
    "        # Get the capacity for the year from the CSV\n",
    "        SAT_capacity = (data.loc[data['Year'] == year, 'SAT Yearly Capacity'].values[0])\n",
    "        UT_capacity = (data.loc[data['Year'] == year, 'FA Yearly Capacity'].values[0])\n",
    "        # Calculate required land area for each site type (Ha)\n",
    "        UT_areas.append(UT_capacity / density_UT)\n",
    "        #print(UT_areas)\n",
    "        SAT_areas.append(SAT_capacity / density_SAT)\n",
    "        Lg_areas.append(SAT_capacity / density_Lg)\n",
    "        \n",
    "    SAT_areas_2040.append(sum(SAT_areas))\n",
    "    UT_areas_2040.append(sum(UT_areas))\n",
    "    Lg_areas_2040.append(sum(Lg_areas))\n",
    "    x+=1\n",
    "    \n",
    "print(x/a)\n",
    "print(y)\n",
    "print((SAT_areas_2040))\n",
    "print(len(UT_areas_2040))\n",
    "print(len(Lg_areas_2040))\n",
    "\n",
    "# Calculate percentiles\n",
    "SAT_Areas_percentiles = np.percentile(SAT_areas_2040, [10, 90])\n",
    "UT_Areas_percentiles = np.percentile(UT_areas_2040, [10, 90])\n",
    "Large_Areas_percentiles = np.percentile(Lg_areas_2040, [10, 90])\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5687e82d",
   "metadata": {},
   "source": [
    "#### Graph Monte-Carlo Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "id": "74ab3104",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "99,646\n",
      "87,683\n",
      "98,671\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\twk54\\AppData\\Local\\Temp\\ipykernel_40336\\2536292622.py:61: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
      "  ax.set_xticklabels([f'{x:,.0f}' for x in ax.get_xticks()])\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 550x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10454\n",
      "0.9565716472163766\n",
      "SAT Future PD 1.0190870625421538, UT future PD: 0.9600632348031791\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "colors = Ovlp4\n",
    "num_cols = 2\n",
    "\n",
    "\n",
    "x_dim, y_dim =Two_x_dim, Two_y_dim\n",
    "\n",
    "plot_name = \"Monte-Carlo\"\n",
    "\n",
    "\n",
    "num_bins = 100\n",
    "\n",
    "def find_max_mode(areas, num_bins):\n",
    "    if not areas:  # Handle empty list case\n",
    "        return None\n",
    "\n",
    "    # Define the bins\n",
    "    min_val = min(areas)\n",
    "    max_val = max(areas)\n",
    "    bins = np.linspace(min_val, max_val, 50 + 1)\n",
    "    \n",
    "    # Digitize the data\n",
    "    binned_areas = np.digitize(areas, bins)\n",
    "    \n",
    "    # Calculate the frequency of each bin\n",
    "    counter = Counter(binned_areas)\n",
    "    \n",
    "    # Find the most common bin\n",
    "    most_common_bin = counter.most_common(1)[0][0]\n",
    "    \n",
    "    # Determine the mode as the midpoint of the most common bin\n",
    "    bin_start = bins[most_common_bin - 1]\n",
    "    bin_end = bins[most_common_bin]\n",
    "    mode = int((bin_start + bin_end) / 2)\n",
    "    \n",
    "    return mode\n",
    "\n",
    "# Example usage\n",
    "UT_mode = find_max_mode(UT_areas_2040, num_bins)\n",
    "UT_mode_str = f\"{UT_mode:,}\"\n",
    "\n",
    "SAT_mode = find_max_mode(SAT_areas_2040, num_bins)\n",
    "SAT_mode_str = f\"{SAT_mode:,}\"\n",
    "\n",
    "Lg_mode = find_max_mode(Lg_areas_2040, num_bins)\n",
    "Lg_mode_str = f\"{Lg_mode:,}\"\n",
    "future_SAT_PD = SAT_future_capacity_DC / SAT_mode #MWDC/Ha\n",
    "future_UT_PD = UT_future_capacity_DC / UT_mode #MWDC/Ha\n",
    "print(UT_mode_str)\n",
    "print(SAT_mode_str)\n",
    "print(Lg_mode_str) \n",
    "\n",
    "# Plotting using seaborn for density plots \n",
    "fig, ax = plt.subplots(figsize=(x_dim, y_dim))\n",
    "\n",
    "sns.kdeplot(Lg_areas_2040, color=C2_1[2], label=f'Large Sites \\nMode: {Lg_mode_str} ha', ax=ax)\n",
    "sns.kdeplot(SAT_areas_2040, color=C2_1[1], label=f'Single Axis Tracked\\nMode: {SAT_mode_str} ha', ax=ax)\n",
    "sns.kdeplot(UT_areas_2040, color=C2_1[0], label=f'Fixed Axis \\nMode: {UT_mode_str} ha', ax=ax)\n",
    "\n",
    "# Modify x-axis labels to represent thousands of hectares\n",
    "ax.set_xlim((0, 250000))\n",
    "ax.set_xticklabels([f'{x:,.0f}' for x in ax.get_xticks()])\n",
    "\n",
    "plt.xlabel(f'Total Land Converted to Solar Sites ({units})', fontdict=font_properties)\n",
    "plt.ylabel('Density', fontdict=font_properties)\n",
    "plt.legend(prop=font_properties)\n",
    "#ax.ticklabel_format(useOffset=False, style='plain')\n",
    "plt.savefig(fr'{figures}\\{plot_name}.jpg', dpi=600, format='jpg')\n",
    "plt.show()\n",
    "\n",
    "print(a)\n",
    "print((n/a))\n",
    "\n",
    "# Plot histograms for slopes\n",
    "plt.figure()\n",
    "plt.hist(SAT_Slopes, bins=300, color=colors[0], alpha=0.6)\n",
    "plt.hist(SAT_Slopes_all, bins=300, color=colors[1], alpha=0.6)\n",
    "\n",
    "\n",
    "print(f'SAT Future PD {future_SAT_PD}, UT future PD: {future_UT_PD}')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f831d001",
   "metadata": {},
   "source": [
    "### Future Energy Density"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "id": "a4e0b97c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Alternative method (better?)\n",
      "SAT future energy density is: 1089. Fixed axis future energy density is: 959\n"
     ]
    }
   ],
   "source": [
    "\n",
    "future_energy_mwh = 95525*1000\n",
    "alt_FA_ED = future_energy_mwh/UT_mode\n",
    "alt_SAT_ED = future_energy_mwh/SAT_mode\n",
    "print(\"Alternative method (better?)\")\n",
    "print(f\"SAT future energy density is: {alt_SAT_ED:.0f}. Fixed axis future energy density is: {alt_FA_ED:.0f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "84045c42",
   "metadata": {},
   "source": [
    "### Current Energy Density"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "id": "04a68103",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SAT Energy Density is: 642.42. UT Energy Density is: 645.7795160261393.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\twk54\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\pyogrio\\raw.py:196: RuntimeWarning: driver GPKG does not support open option DRIVER\n",
      "  return ogr_read(\n",
      "c:\\Users\\twk54\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\pyogrio\\raw.py:196: UserWarning: Measured (M) geometry types are not supported. Original type 'Measured 3D MultiPolygon' is converted to 'MultiPolygon Z'\n",
      "  return ogr_read(\n"
     ]
    }
   ],
   "source": [
    "hours_per_year = 8766\n",
    "feature_class = 'Digitized_Fences'\n",
    "Area_data = gpd.read_file(f'{gdb_path}', layer='Digitized_Fences', driver='OpenFileGDB')\n",
    "SAT = gdf[gdf['Site_Type'] == 'SAT']\n",
    "UT = gdf[gdf['Site_Type'] == 'UT']\n",
    "SAT_Area = SAT['Shape_Area'].sum()/10000\n",
    "UT_Area = UT['Shape_Area'].sum()/10000\n",
    "SAT_Cap = SAT['Capacity_MW'].sum()\n",
    "UT_Cap = UT['Capacity_MW'].sum()\n",
    "\n",
    "SAT_Energy = SAT_Cap * hours_per_year * (sat_mean/100)\n",
    "UT_Energy= UT_Cap * hours_per_year *(fa_mean / 100)\n",
    "\n",
    "SAT_energy_density = SAT_Energy/SAT_Area\n",
    "UT_energy_density = UT_Energy / UT_Area\n",
    "\n",
    "\n",
    "sites = ['SAT Sites', 'UT Sites']\n",
    "vals = [SAT_energy_density, UT_energy_density]\n",
    "print(f'SAT Energy Density is: {SAT_energy_density:.2f}. UT Energy Density is: {UT_energy_density}.')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "57df57db",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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